Trabajo elaborado para la asignatura “Programación y manejo de datos en la era del Big Data” de la Universitat de València durante el curso 2021-2022. El repo del trabajo está aquí.

La página web de la asignatura y los trabajos de mis compañeros pueden verse aquí.


1. Introducción

La Formula 1 es uno de los deportes/espectaculos más importantes del mundo, logrando a lo largo de sus historia captar a millones y millones de espectadores. Nuetro trabajo pretende plasmar la grandeza de la Fórmula 1 a través de distintos datos que pueden verse a contimnuación, tanto presentes como pasados. Además, parte del trabajo se ha centrado en la figura de Fernando Alonso, el piloto con mayor importancia en España, y uno de los más relevantes en la historia de la Formula 1.
En relación con la asignatura, la elección del tema se debe a lo fundamental que son los datos en este deporte, tanto para la elaboración de estrategias, el diseño de los coches o incluso de las pistas. Debido a todas estas razones, creemos que la F1, a pesar de no ser un tema relacionado con la economía, encaja perfectamente con la asignatura.

THE PLAN

2. Datos

Hemos encontrado en kaggle bastantes conjuntos de datos con los que poder trabajar, pero especialmente este, que posee gran variedad de datos en lo referente a pilotos, resultados, circuitos, tiempos, etc.

2.1. Procesando los datos

A continuación se muestra toda la manipulación de los datos, a partir del conjunto de datos mostrado anteriormente, y que posteriormente son utilizados para la realización de los distintas partes que forman el trabajo.

#---PREPARACION DE LOS DATOS
tiempos <- rio::import(file = "./datos/lap_times.csv")
pilotos <- rio::import(file = "./datos/drivers.csv")
resultados <- rio::import(file = "./datos/results.csv")
carreras <- rio::import(file = "./datos/races.csv")
escuderias <- rio::import(file = "./datos/constructors.csv")
escuderias2 <- rio::import(file = "./datos/constructor_standings.csv")
result_escuderias <- rio::import(file = "./datos/constructor_results.csv")
circuitos <- rio::import(file = "./datos/circuits.csv")

#----------------------------------------
#capitulo oscuro del deporte
#creo df de muertes de formula 1
muertesf1 <- data.frame(
  "orden" = 1:42,
  "driverRef" = c("Chet Miller", "Carl Scaraborough", "Onofre Marimon", "Manny Ayulo", "Bill Vukovich", "Alberto Ascari","Eugenio Castellotti", "Keith Andrews", "Pat O'Connor", "Luigi Musso", "Peter Collins", "Stuart Lewis-Evans", "Jerry Unser", "Bob Cortner", "Ivor Bueb", "Chris Bristow", "Alan Stacey", "Giulio Cabianca", "Wolfgang von Trips", "Carel Godin de Beaufort", "John Taylor", "Lorenzo Bandini", "Bob Anderson", "Jo Schlesser", "Gerhard Mitter", "Piers Courage", "Jochen Rindt", "Jo Siffert", "Roger Williamson", "François Cevert", "Peter Revson", "Helmuth Koinigg", "Mark Donohue", "Tom Pryce", "Ronnie Peterson", "Patrick Depailler", "Gilles Villeneuve", "Riccardo Paletti", "Elio de Angelis", "Roland Ratzenberger", "Ayrton Senna", "Jules Bianchi"),
  "nationality" = c("American", "American", "Argentine", "American", "American", "Italian","Italian", "American", "American", "Italian", "British", "British", "American", "American", "British", "British", "British", "Italian", "German", "Dutch", "British", "Italian" , "British", "French", "German", "British", "Austrian", "Swiss", "British", "French", "American", "Austrian", "American", "British", "Swedish", "French", "Canadian", "Italian", "Italian", "Austrian", "Brazilian", "French"),
  "fecha_muerte" = c(1953, 1953, 1954, 1955, 1955, 1955, 1957, 1957, 1958,1958,1958,1958,1959,1959,1959,1960, 1960,1961,1961,1964,1966, 1967, 1967,1968,1969, 1970,1970,1971,1973,1973,1974,1974,1975,1977,1978,1980,1982,1982,1986,1994,1994,2014))
#no pongo las comillas en los años para que se creen directamente como observaciones numericas

#creo un df con todos los años para luego fusionarlo, ya que no hay muertes todos los años 
anyos <- data.frame(
  "orden" = 1:71,
  "año" = c(1950:2020))

#sumatorio de las muertes por año
muertes_anyo <- muertesf1 %>% group_by(fecha_muerte) %>% mutate(muertesxanyo = sum(n())) %>% distinct(fecha_muerte, muertesxanyo) 

#fusiono los 2 dfs para que tenga en cuenta los años donde no hay muertes
muertesf1_final <- full_join(muertes_anyo, anyos, c("fecha_muerte" = "año")) %>% select(fecha_muerte,muertesxanyo) %>% arrange(fecha_muerte)

#convierto los N/A en 0, es decir, cuando no hay observaciones, ha habido 0 muertes
muertesf1_final[is.na(muertesf1_final)] <- 0

#------------------------------------------------
#str(resultados)
#str(pilotos)
#pilotos[, c(1)] <- sapply(pilotos[, c(1)], as.numeric)
#resultados[, c(3,6,9)] <- sapply(resultados[, c(3,6,9)], as.numeric)

#----------------------------------------------------------------------------------

#numero de carreras que ha corrido cada piloto

n_carreras <- resultados %>% group_by(driverId) %>% mutate(numero_carreras = sum(n())) %>% distinct(numero_carreras) %>% arrange(desc(numero_carreras)) 

n_carreras_nom <- full_join(n_carreras, pilotos, c ("driverId" = "driverId")) %>% select(driverId, driverRef, numero_carreras)  %>%  filter(numero_carreras >= 202 ) #los 20 qque mas carreras tienen (no funciona usar slice_max)

#--------------------------------------------------------------------------------
#nacionalidad de los pilotos a lo largo de la historia 
#mapa
nacionalidad <- pilotos %>% group_by(nationality) %>% mutate(numero_compatriotas = sum(n())) %>% distinct(numero_compatriotas) %>% arrange(desc(numero_compatriotas)) %>% 
  str_replace_all(., "British", "United Kingdom") %>% 
  str_replace_all(., "American", "United States") %>% 
  str_replace_all(., "Italian", 'Italy') %>% 
  str_replace_all(., "French" , 'France') %>% 
  str_replace_all(., "German" , 'Germany') %>% 
  str_replace_all(., "Brazilian" , 'Brazil') %>% 
  str_replace_all(., "Argentine" , 'Argentina') %>% 
  str_replace_all(., "Swiss" , 'Switzerland') %>% 
  str_replace_all(., "Belgian" , 'Belgium') %>% 
  str_replace_all(., "South African" , 'South Africa') %>% 
  str_replace_all(., "Japanese" , 'Japan') %>% 
  str_replace_all(., "Australian" , 'Australia') %>%
  str_replace_all(., "Dutch" , 'Netherlands') %>%
  str_replace_all(., "Spanish" , 'Spain') %>%
  str_replace_all(., "Austrian" , 'Austria') %>%
  str_replace_all(., "Canadian" , 'Canada') %>%
  str_replace_all(., "Swedish" , 'Sweden') %>%
  str_replace_all(., "Finnish" , 'Finland') %>%
  str_replace_all(., "New Zealander" , 'New Zealand') %>%
  str_replace_all(., "Mexican" , 'Mexico') %>%
  str_replace_all(., "Irish" , 'Ireland') %>%
  str_replace_all(., "Danish" , 'Denmark') %>%
  str_replace_all(., "Portuguese" , 'Portugal') %>%
  str_replace_all(., "Monegasque" , 'France') %>%
  str_replace_all(., "Rhodesian" , 'Zimbabwe') %>%
  str_replace_all(., "Uruguayan" , 'Uruguay') %>%
  str_replace_all(., "Russian" , 'Russia') %>%
  str_replace_all(., "Colombian" , 'Colombia') %>%
  str_replace_all(., "Venezuelan" , 'Venezuela') %>%
  str_replace_all(., "East German" , 'German') %>%
  str_replace_all(., "Indian" , 'India') %>%
  str_replace_all(., "Thai" , 'Thailand') %>%
  str_replace_all(., "Polish" , 'Poland') %>%
  str_replace_all(., "Monegasque" , 'France') %>%
  str_replace_all(., "Hungarian" , 'Hungary') %>%
  str_replace_all(., "Czech" , 'Czech Rep.') %>%
  str_replace_all(., "Malaysian" , 'Malaysia') %>%
  str_replace_all(., "Chilean" , 'Chile') %>%
  str_replace_all(., "Liechtensteiner" , 'Switzerland') %>%
  str_replace_all(., "American-Italian" , 'United States') %>%
  str_replace_all(., "Argentine-Italian" , 'Argentina') %>%
  str_replace_all(., "Indonesian" , 'Indonesia')

paises_normales <- c("United Kingdom", "United States", "Italy", "France", "Germany", "Brazil", "Argentina", "Switzerland", "Belgium", "South Africa", "Japan", "Australia", "Netherlands", "Spain", "Austria", "Canada", "Sweden", "Finland", "New Zealand", "Mexico", "Ireland", "Denmark", "Portugal", "France", "Zimbabwe", "Uruguay", "Russia", "Colombia", "Venezuela", "Germany", "India", "Thailand", "Poland", "Hungary", "Czech Rep.", "Malaysia", "Chile", "Switzerland", "United States", "Argentina", "Indonesia" )

nacionalidad <- pilotos %>% group_by(nationality) %>% 
  mutate(numero_compatriotas = sum(n())) %>% 
  distinct(numero_compatriotas) %>% 
  arrange(desc(numero_compatriotas)) %>% 
  add_column(paises_normales) %>%  
  group_by(paises_normales) %>% 
  mutate(total_pilotos = sum(numero_compatriotas)) %>%  
  distinct(paises_normales, total_pilotos)

data(World)
world <- World; rm(World)

world <- world[!(world$name %in% c('Greenland', 'Antarctica')),]

pilotos_por_paises <- full_join(nacionalidad, world, c( "paises_normales" = "name")) 

pilotos_por_paises$country <- NULL

pilotos_por_paises[is.na(pilotos_por_paises)] <- 0

gg_pilotos_por_paises <- ggplot(data = pilotos_por_paises, aes(geometry = geometry)) + geom_sf() +
       labs(title = "Pilotos por país",
            caption = "Menor intensidad de color en aquellos paises con más pilotos")

gg_pilotos_por_paises <- gg_pilotos_por_paises + 
  geom_sf(aes(fill = total_pilotos)) + 
  theme(plot.subtitle = element_text(colour = "white"),
    plot.caption = element_text(colour = "white"),
    axis.ticks = element_line(linetype = "blank"),
    panel.grid.major = element_line(linetype = "blank"),
    panel.grid.minor = element_line(linetype = "blank"),
    axis.text = element_text(colour = "gray13"),
    plot.title = element_text(size = 20,
        colour = "white", hjust = 0.5), legend.text = element_text(colour = "white"),
    legend.title = element_text(colour = "white"),
    panel.background = element_rect(fill = "gray13"),
    plot.background = element_rect(fill = "gray13"),
    legend.background = element_rect(fill = "gray13")) +labs(fill = "Nª de pilotos") + theme(panel.background = element_rect(colour = "gray13"),
    plot.background = element_rect(colour = "gray13")) + theme(panel.grid.major = element_line(colour = NA),
    panel.background = element_rect(linetype = "solid"),
    plot.background = element_rect(linetype = "solid")) + scale_fill_gradient2(low = "white", mid = "pink", high = "red", midpoint = .02)

#------------------------------------------------------------------------------
# numero de victorias por piloto
victorias <- resultados %>% filter(position == "1") %>% 
  group_by(driverId) %>% 
  mutate(n_victorias = sum(n())) %>% 
  distinct(n_victorias) %>% 
  arrange(desc(n_victorias))

#aqui fusiono con el df de pilotos para que aparezca el nombre y no sólo el ID del piloto en cuestion, y hago lo mismo que en el apartado de arriba para ordenar
victorias_con_nombre <- full_join(victorias, pilotos, c ("driverId" = "driverId")) %>% 
  select(driverId, nationality, driverRef, n_victorias)  
mas_victorias <- victorias_con_nombre %>%  filter(n_victorias >= 25 ) 

#victorias 2
victorias2 <- full_join(pilotos, resultados, c("driverId" = "driverId")) %>% 
  full_join(., carreras, c("raceId" = "raceId")) %>% 
  select(driverId, driverRef, position, year, date) %>% 
  filter(position == 1) %>% 
  group_by(driverRef) %>% 
  mutate(victoria_num = sum( NN = n())) %>% 
  filter(victoria_num >= 20) %>% 
  arrange(victoria_num, date) %>% 
  mutate( suma_vic = cumsum(position))

#-------------------------------------------------

#resultado medio

resultados[, c(7)] <- sapply(resultados[, c(7)], as.numeric)
resultados[is.na(resultados)] <- 25 

#resultado_medio <-  full_join(pilotos, resultados,  c ("driverId" = "driverId")) %>% 
#select(driverId, driverRef, position) %>% 
 # group_by(driverId) %>% 
  #mutate(result_medio = mean(position)) %>% 
  #distinct (driverId, driverRef, result_medio) %>% arrange(result_medio)

#resultado medio en clasificacion
resultado_medio_clas <-  full_join(pilotos, resultados,  c ("driverId" = "driverId")) %>% 
  select(driverId, driverRef, grid) %>% 
  group_by(driverId) %>% 
  mutate(result_medio_clas = mean(grid)) %>% 
  distinct (driverId, driverRef, result_medio_clas)  %>% 
  filter(result_medio_clas > 0) %>% 
  arrange(result_medio_clas)  


#numero de vueltas liderando


#puntos por carrera (puntos/carrera)

puntos_x_carrera <-  full_join(pilotos, resultados,  c ("driverId" = "driverId")) %>% select(driverId, driverRef, points) %>% full_join(., n_carreras,  c ("driverId" = "driverId")) %>% group_by(driverId) %>% mutate(total_puntos = sum(points)) %>% distinct(driverId, driverRef, numero_carreras, total_puntos) %>% mutate(media_puntos = total_puntos/numero_carreras) %>% arrange(desc(media_puntos))

#-------------------------------
#remontadas

#mas posiciones remontadas en una carrera gran premio


resultados[, c(6,9)] <- sapply(resultados[, c(6,9)], as.numeric) #transformo variables grid y positionOrder en numerico
str(resultados) # para comprobarlo
#> 'data.frame':    25140 obs. of  18 variables:
#>  $ resultId       : int  1 2 3 4 5 6 7 8 9 10 ...
#>  $ raceId         : int  18 18 18 18 18 18 18 18 18 18 ...
#>  $ driverId       : int  1 2 3 4 5 6 7 8 9 10 ...
#>  $ constructorId  : int  1 2 3 4 1 3 5 6 2 7 ...
#>  $ number         : chr  "22" "3" "7" "5" ...
#>  $ grid           : num  1 5 7 11 3 13 17 15 2 18 ...
#>  $ position       : num  1 2 3 4 5 6 7 8 25 25 ...
#>  $ positionText   : chr  "1" "2" "3" "4" ...
#>  $ positionOrder  : num  1 2 3 4 5 6 7 8 9 10 ...
#>  $ points         : num  10 8 6 5 4 3 2 1 0 0 ...
#>  $ laps           : int  58 58 58 58 58 57 55 53 47 43 ...
#>  $ time           : chr  "1:34:50.616" "+5.478" "+8.163" "+17.181" ...
#>  $ milliseconds   : chr  "5690616" "5696094" "5698779" "5707797" ...
#>  $ fastestLap     : chr  "39" "41" "41" "58" ...
#>  $ rank           : chr  "2" "3" "5" "7" ...
#>  $ fastestLapTime : chr  "1:27.452" "1:27.739" "1:28.090" "1:28.603" ...
#>  $ fastestLapSpeed: chr  "218.300" "217.586" "216.719" "215.464" ...
#>  $ statusId       : int  1 1 1 1 1 11 5 5 4 3 ...
#mayores remontadas de la historia, se resta posicion de salida - posicion final
puestos_remontados <- resultados %>% mutate(remontados = grid - positionOrder) %>% 
  select(raceId, driverId, grid, positionOrder, remontados) 


#de toda la historia
circuitos_gp <- full_join(carreras, circuitos, c("circuitId" = "circuitId")) %>% 
  select(circuitId, name.y, raceId, year)

ptos_remont_carrera <- inner_join(puestos_remontados, circuitos_gp)

puestos_remont_piloto <- full_join(pilotos, ptos_remont_carrera, c("driverId" = "driverId")) %>% 
  slice_max(remontados, n=10) %>% 
  select(driverId, driverRef,name.y,year, raceId, grid, positionOrder, remontados)

# de la hisotoria reciente
circuitos_gp_recient <- full_join(carreras, circuitos, c("circuitId" = "circuitId")) %>% 
  select(circuitId, name.y, raceId, year) %>% 
  filter(year >= 1995)

ptos_remont_carrera_recient <- inner_join(puestos_remontados, circuitos_gp_recient)

puestos_remont_piloto_recient <- full_join(pilotos, ptos_remont_carrera_recient, c("driverId" = "driverId")) %>% 
  slice_max(remontados, n=10) %>% 
  select(driverId, driverRef, name.y, year,raceId, grid, positionOrder, remontados) %>% 
  slice(1:4,6:8,10) %>% 
  arrange(desc(remontados))


#--------------------------------
#mundiales por naionalidad y piloto
campeones <- full_join(pilotos, resultados, c("driverId" = "driverId")) %>% 
  full_join(., carreras, c("raceId" = "raceId")) %>% 
  select(driverId, driverRef, nationality, constructorId, points, year, round) %>% 
  full_join(., escuderias, c("constructorId" = "constructorId")) %>% 
  select(driverId, driverRef, nationality.x, constructorId, points, year, name, round) %>%  
  group_by(year, driverRef) %>%  
  mutate(puntos_totales = cumsum(points)) %>% 
  ungroup() %>% 
  group_by(year) %>% 
  slice_max(puntos_totales, n=1) %>% 
  ungroup() %>% group_by(driverRef) %>% 
  mutate(total_campeonatos = sum(NN = n())) %>% 
  distinct(driverRef, nationality.x, total_campeonatos) %>% 
  arrange(nationality.x, total_campeonatos)

#----------------------------------
#temporada 2021 tabla

pilotos_2021 <- full_join(carreras, resultados, c("raceId" = "raceId") ) %>% 
  full_join(.,pilotos, c("driverId"="driverId")) %>% 
  filter(year=="2021") %>% 
  select(forename,surname, year) %>% 
  distinct(forename,surname) %>% 
  na.omit() %>% 
  arrange(surname) %>% 
  str_replace_all(., "é", "é") %>% 
  str_replace_all(., "ä", "ä") %>% 
  str_replace_all(., "ö", "ö")

nombres_normales <- c("Alonso", "Bottas", "Gasly", "Giovinazzi", "Hamilton", "Latifi", "Leclerc", "Mazepin", "Norris", "Ocon", "Pérez", "Räikkönen", "Ricciardo", "Russell", "Sainz", "Schumacher", "Stroll", "Tsunoda", "Verstappen", "Vettel")

pilotos_2021 <-  full_join(carreras, resultados, c("raceId" = "raceId") ) %>% 
  full_join(.,pilotos, c("driverId"="driverId")) %>% 
  filter(year=="2021") %>% 
  select(forename,surname, year) %>% 
  distinct(forename,surname) %>% 
  na.omit() %>% 
  arrange(surname) %>% 
  add_column(nombres_normales) 

#--------------------------
#temporada 2021 templos

carreras_21 <- full_join(carreras,circuitos, c("circuitId" = "circuitId")) %>%
  filter(year=="2021") %>%
  select(round, name.x, name.y, date, location,country, lat, lng, alt) %>%
  arrange(round) %>% 
  mutate(round2 = round) 

carreras_21_v2 <- carreras_21[, c(1, 4, 10, 2, 3, 5, 6, 7, 8, 9)]
carreras_21_v2 <- carreras_21_v2%>%  unite(. ,variables, c(1, 5, 7), sep = "; ")


#----------------------------------
#el nano contra todos


alovsall <- full_join(pilotos, resultados, c ("driverId" = "driverId")) %>%  
  select(driverRef, resultId, raceId, constructorId, position, points) %>% 
  full_join(., escuderias, c ("constructorId" = "constructorId")) %>% 
  select(driverRef, resultId, raceId, constructorId, position, position, points, name) %>% 
  full_join(., carreras, c ("raceId" = "raceId")) %>% 
  select(driverRef, resultId, raceId, constructorId, position, position, points, name.x, year, round)

#alo_vs_marques <- alovsall %>% filter(year == 2001, driverRef %in% c("alonso", "marques"), round <= 14)

alo_vs_trulli <- alovsall %>% filter(year %in% c(2003, 2004), driverRef %in% c("alonso", "trulli")) %>% slice(1:15, 17:67) %>% group_by(driverRef, year) %>% mutate(puntos_acumulados = cumsum(points)) %>% ungroup()

alo_vs_fisichella <- alovsall %>% filter(year %in% c(2005, 2006), driverRef %in% c("alonso", "fisichella"))  %>% group_by(driverRef, year) %>% mutate(puntos_acumulados = cumsum(points)) %>% ungroup()

alo_vs_hamilton <- alovsall %>% filter(year %in% c(2007) ,driverRef %in% c("alonso", "hamilton"))  %>% group_by(driverRef, year) %>% mutate(puntos_acumulados = cumsum(points)) %>% ungroup()

alo_vs_piquet <- alovsall %>% filter(year %in% c(2008, 2009), driverRef %in% c("alonso", "piquet_jr")) %>% slice(1:28, 36:63) %>% group_by(driverRef, year) %>% mutate(puntos_acumulados = cumsum(points)) %>% ungroup()

#alo_vs_grosjean <- alovsall %>% filter(year == 2009, driverRef %in% c("alonso", "grosjean"), round >= 11)

alo_vs_massa <- alovsall %>% filter(year %in% c(2010, 2011, 2013), driverRef %in% c("alonso", "massa")) %>% group_by(driverRef, year) %>% mutate(puntos_acumulados = cumsum(points)) %>% ungroup()

alo_vs_raikkonen <- alovsall %>% filter(year == 2014, driverRef %in% c("alonso", "raikkonen")) %>% group_by(driverRef, year) %>% mutate(puntos_acumulados = cumsum(points)) %>% ungroup()

alo_vs_button <- alovsall %>% filter(year %in% c(2015, 2016), driverRef %in% c("alonso", "button")) %>% group_by(driverRef, year) %>% mutate(puntos_acumulados = cumsum(points)) %>% ungroup()

alo_vs_vandoorne <- alovsall %>% filter(year %in% c(2017, 2018), driverRef %in% c("alonso", "vandoorne")) %>% slice(1:45, 47:81) %>% group_by(driverRef, year) %>% mutate(puntos_acumulados = cumsum(points))%>% ungroup() 

alo_vs_ocon <- alovsall %>% filter(year == 2021, driverRef %in% c("alonso", "ocon")) %>% group_by(driverRef, year) %>% mutate(puntos_acumulados = cumsum(points)) %>% ungroup()

ALO_VS_ALL <- full_join(alo_vs_trulli, alo_vs_fisichella, c("driverRef"= "driverRef", "resultId" = "resultId", "raceId" = "raceId", "constructorId" = "constructorId", "position" = "position", "points" = "points", "name.x" = "name.x", "year" = "year", "round" = "round" , "puntos_acumulados" = "puntos_acumulados")) %>% 
  full_join(., alo_vs_hamilton, c("driverRef"= "driverRef", "resultId" = "resultId", "raceId" = "raceId", "constructorId" = "constructorId", "position" = "position", "points" = "points", "name.x" = "name.x", "year" = "year", "round" = "round", "puntos_acumulados" = "puntos_acumulados")) %>% 
  full_join(., alo_vs_piquet, c("driverRef"= "driverRef", "resultId" = "resultId", "raceId" = "raceId", "constructorId" = "constructorId", "position" = "position", "points" = "points", "name.x" = "name.x", "year" = "year", "round" = "round", "puntos_acumulados" = "puntos_acumulados")) %>% 
  full_join(., alo_vs_massa, c("driverRef"= "driverRef", "resultId" = "resultId", "raceId" = "raceId", "constructorId" = "constructorId", "position" = "position", "points" = "points", "name.x" = "name.x", "year" = "year", "round" = "round", "puntos_acumulados" = "puntos_acumulados"))  %>% 
  full_join(., alo_vs_raikkonen, c("driverRef"= "driverRef", "resultId" = "resultId", "raceId" = "raceId", "constructorId" = "constructorId", "position" = "position", "points" = "points", "name.x" = "name.x", "year" = "year", "round" = "round", "puntos_acumulados" = "puntos_acumulados")) %>% 
  full_join(., alo_vs_button, c("driverRef"= "driverRef", "resultId" = "resultId", "raceId" = "raceId", "constructorId" = "constructorId", "position" = "position", "points" = "points", "name.x" = "name.x", "year" = "year", "round" = "round", "puntos_acumulados" = "puntos_acumulados")) %>% 
  full_join(., alo_vs_vandoorne, c("driverRef"= "driverRef", "resultId" = "resultId", "raceId" = "raceId", "constructorId" = "constructorId", "position" = "position", "points" = "points", "name.x" = "name.x", "year" = "year", "round" = "round", "puntos_acumulados" = "puntos_acumulados")) %>% 
  full_join(., alo_vs_ocon, c("driverRef"= "driverRef", "resultId" = "resultId", "raceId" = "raceId", "constructorId" = "constructorId", "position" = "position", "points" = "points", "name.x" = "name.x", "year" = "year", "round" = "round", "puntos_acumulados" = "puntos_acumulados"))


#------------------------------
#el nano contra hamilton
fotos_ALO_vs_HAM <- c("./imagenes/pilotos/alonso.png", "./imagenes/pilotos/hamilton.png")
fotos_esp_ing <- c("./imagenes/paises/espanya.png", "./imagenes/paises/uk.png")
n_carreras_alo_ham <- n_carreras_nom %>% filter(driverRef %in% c("alonso", "hamilton"))

n_victorias_alo_ham <- victorias_con_nombre %>% filter(driverRef %in% c("alonso", "hamilton"))

alo_vs_ham <- full_join(n_carreras_alo_ham, n_victorias_alo_ham, c("driverRef"= "driverRef")) %>% select( driverRef, numero_carreras, n_victorias) %>% add_column(fotos_esp_ing, fotos_ALO_vs_HAM) 

3. The plan

3.1 Un paseo por la historia

El nacimiento de la Fórmula 1 se remonta a 1950, donde la FIA, Federación Internacional del Automóvil, decidió unir varios grandes premios de diferentes países, para crear el ampeonato undial de pilotos. El primer gran premio se celebró en el circuito de Silverstone, el 13 de mayo de 1950. Desde entonces, La Fórmula 1 ha ido evolucionando, tanto a nivel de coches, circuitos, o logos, para acabar siendo el espectáculo que es en la actualidad.

Coches

El automóvil puede ser el elemento que mayores cambios ha experimetnado con el paso del tiempo. En los años 50, las escuderias más destacables eran los propios fabricantes de vehículos, por ejemplo Maserati o Alfa Romeo. Los coches en esta década se caracterizaban por tener un motor frontal, a diferencia de lo que pasaría en años posteriores.

A partir de la década de los 60, se comenzaron a utilizar vehículos con motor central, suponiendo una importante mejora en el desempeño de los coches. Además, se dio peso a otros aspectos, como la composición del chasis, siendo la escudería lotus pionera en introducir los chasis monocasco de aluminio.

En los 70, la aerodinámica pasó a tener un papel fundamental, introduciendose el efecto suelo, q permitía rodar a mayor velocidad en las curvas. Entre 1980 y 1990 destacan las ayudas electrónicas, como por ejemplo la suspensión activa, el cambio de marchas semi-automático, o el control de tracción.

Desde entonces, hasta la actualidad, los cammbios en los coches no han sido tan relevantes, produciendose un equilibrio en las mejoras de las distintas partes del vehículo: aerodinámica, chasis y motor.

coche1950 <- image_read("./imagenes/coches/1950.jpg") %>% image_scale(., "500") %>%  image_annotate(., "1950", size = 40, gravity = "southwest", color = "white")
coche1960 <- image_read("./imagenes/coches/1960.jpg") %>% image_scale(., "500")%>%  image_annotate(., "1960", size = 40, gravity = "southwest", color = "white")
coche1970 <- image_read("./imagenes/coches/1970.jpg") %>% image_scale(., "500")%>%  image_annotate(., "1970", size = 40, gravity = "southwest", color = "white")
coche1980 <- image_read("./imagenes/coches/1980.jpg") %>% image_scale(., "500")%>%  image_annotate(., "1980", size = 40, gravity = "southwest", color = "white")
coche1990 <- image_read("./imagenes/coches/1990.jpg") %>% image_scale(., "500")%>%  image_annotate(., "1990", size = 40, gravity = "southwest", color = "white")
coche2005 <- image_read("./imagenes/coches/2005.jpg") %>% image_scale(., "500")%>%  image_annotate(., "2005", size = 40, gravity = "southwest", color = "white")
coche2020 <- image_read("./imagenes/coches/2020.jpg") %>% image_scale(., "500")%>%  image_annotate(., "2020", size = 40, gravity = "southwest", color = "white")
coches <- c(coche1950, coche1960, coche1970, coche1980, coche1990, coche2005, coche2020)

image_animate(image_scale(coches), fps = 0.5)

Logos

Logo de 1985 a 1986

Aunque no tan espectacular, también se ha producido ua evolución de la estética del logo, pasando de una imagen más cargada, en la que aparece hasta el símbolo de la FIA, al logo actual, compuesto por la F y el 1.

Logo de 1950 a 1986

Logo de 1987 a 1993

Logo de 1987 a 1993

Logo de 1994 a 2017

Logo de 1994 a 2017

Logo Actual

LOGO desde 2018 hasta la actualidad

Capítulo oscuro del deporte

Hay que ser conscientes que, de igual manera que la Formula 1 es espectacular, sus pilotos se exponen a un gran riesgo, debido a las velocidades que alcanzan. Prueba de ello es, el gráfico mostrado a continuación, en el cual se refleja todas las muertes de pilotos producidas en la F1. El total es de 42, siendo la peor decada los años 50, produciendose 15 fallecimientos.

Sin embargo, la tendencia ha sido claramente negativa, debido a las diferentes medidas de seguridad que se han ido incorporando. La última introducción ha sido el halo, centrado en evitar golpes o aplastamientos de la cabeza del piloto. Otras mejoras destacadas han sido, las proteciones y grava en los circuitos, los monos ignífugos o la reglamentación en el diseño del casco.

gg_muertes <- ggplot(muertesf1_final, aes(x = fecha_muerte, y = muertesxanyo )) +  geom_bar(stat = "identity", fill = "white", colour = "white") + geom_smooth(colour = "cyan", se = FALSE) + labs(x = "Año" , y = "Número de muertes")  + theme(axis.line = element_line(colour = "white"),
    axis.ticks = element_line(colour = "white"),
    panel.grid.major = element_line(colour = "gray13"),
    panel.grid.minor = element_line(colour = "gray13"),
    axis.title = element_text(colour = "white"),
    axis.text = element_text(colour = "white"),
    plot.title = element_text(colour = "white"),
    panel.background = element_rect(fill = "gray13",
        colour = "white"), plot.background = element_rect(fill = "gray13")) +labs(colour = "white") + theme(panel.grid.major = element_line(colour = "gray38",
    linetype = "dotted"), panel.grid.minor = element_line(colour = NA),
    plot.title = element_text(size = 25,
        hjust = 0.5)) +labs(title = "Accidentes mortales por año") + geom_text(data = data.frame(x = 2004.10522642875, y = 0.237450516942241, 
    label = "Tendencia negativa"), mapping = aes(x = x, y = y, 
    label = label), colour = "cyan", inherit.aes = FALSE, size = 3)

ggplotly(gg_muertes)

3.2 Lo más destacado

Audencias

audiencias <- rio::import(file = "./datos/audienciasF1.csv")


gg_audiencias <- ggplot(audiencias, aes(x=year, y= numero_espectadores)) +
  geom_area() +
  geom_point( size=1.5, color="cyan", fill=alpha("cyan", 8), shape=21, stroke=2) +  
  geom_line(color="#69b3a2", size=1) +
  scale_x_continuous(
    breaks = seq(2004, 2020, 1),
    limits = c(2003, 2021)) + labs(x = "Año", y = "Numero de espectadores" )  + theme(panel.background = element_rect(fill = "gray13"),
    plot.background = element_rect(fill = "gray13")) + theme(axis.line = element_line(colour = "white"),
    panel.grid.major = element_line(colour = "gray20"),
    panel.grid.minor = element_line(colour = "gray20"),
    axis.text = element_text(colour = "white"),
    legend.position = "none") + theme(axis.title = element_text(colour = "white"),
    plot.title = element_text(colour = "white",
        hjust = 0.5)) +labs(title = "Evolución de la audiencia",
    colour = "white") + theme(axis.text.x = element_text(size = 4))


ggplotly(gg_audiencias)

Presupuestos

presupuestos <- read_excel("datos/presupuestos.xlsx")

gg_presup <- ggplot(presupuestos, aes(year, Presupuesto, color = Escuderia)) + 
  geom_point() + geom_line() + 
  labs(x = "Año", y = "Presupuesto en €" ) +
    scale_x_continuous(
    breaks = seq(2015, 2023, 1),
    limits = c(2014, 2024)) + 
  scale_y_continuous( breaks = seq(0, 700000000, 100000000),
    limits = c(0, 600000000))  + theme(axis.ticks = element_line(colour = "white"),
    panel.grid.major = element_line(colour = "white",
        linetype = "blank"), panel.grid.minor = element_line(colour = "white",
        linetype = "blank"), axis.title = element_text(size = 14,
        face = "bold", colour = "cyan", vjust = 0.75),
    axis.text = element_text(colour = "white"),
    plot.title = element_text(size = 16,
        face = "bold", colour = "cyan", hjust = 0.5,
        vjust = 0.75), legend.text = element_text(face = "bold",
        colour = "cyan"), legend.title = element_text(size = 13,
        face = "bold", colour = "cyan"),
    panel.background = element_rect(fill = "gray13",
        colour = "white"), plot.background = element_rect(fill = "gray13"),
    legend.key = element_rect(fill = "gray13"),
    legend.background = element_rect(fill = "gray13")) +labs(title = "PRESUPUESTO DE CADA EQUIPO POR TEMPORADA") + theme(panel.grid.major = element_line(colour = NA),
    panel.grid.minor = element_line(colour = NA),
    axis.title = element_text(size = 11),
    plot.title = element_text(size = 14),
    legend.text = element_text(size = 9),
    legend.title = element_text(size = 11),
    panel.background = element_rect(fill = "gray13",
        colour = NA), plot.background = element_rect(fill = "gray13",
        colour = NA)) + theme(legend.key = element_rect(fill = "gray13"),
    legend.background = element_rect(fill = "gray13")) + theme(axis.line = element_line(colour = "gray20",
    linetype = "solid"), panel.grid.major = element_line(colour = "gray15",
    linetype = "solid"))
 
ggplotly(gg_presup) #para que sea interactivo

Nacionalidades

Mapa

ggplotly(gg_pilotos_por_paises)

Tabla

set_flextable_defaults(
  font.size = 15,
  font.color = "white",
  table.layout = "fixed", 
  background.color = "gray13")

flex_piloto <- flextable(nacionalidad)

small_border = fp_border(color="gray50", width = 2)

flex_piloto1 <- border_outer(flex_piloto, part="all", border = small_border )


theme_vader(flex_piloto1)

Pilotos con más victorias

ggplot(victorias2, aes(x = reorder(driverRef,suma_vic), suma_vic)) + 
  geom_line(size=5, colour = "cyan") +
  coord_flip() +
  theme(plot.subtitle = element_text(hjust = 0.5),
    plot.caption = element_text(size = 15,
        colour = "white", hjust = 0), axis.line = element_line(colour = "gray20",
        linetype = "solid"), panel.grid.major = element_line(linetype = "blank"),
    panel.grid.minor = element_line(linetype = "blank"),
    axis.title = element_text(colour = "cyan"),
    axis.text = element_text(colour = "cyan"),
    axis.text.x = element_text(colour = "white"),
    axis.text.y = element_text(colour = "white"),
    plot.title = element_text(size = 17,
        colour = "white", hjust = 0.5), panel.background = element_rect(fill = "gray13",
        colour = "gray13", linetype = "solid"),
    plot.background = element_rect(fill = "gray13",
        colour = "gray13", linetype = "solid")) +labs(title = 'Pilotos con más victorias' ,x = "Pilotos",
    y = "Nº victorias") + theme(plot.subtitle = element_text(colour = "gray50"),
    plot.title = element_text(face = "bold",
        colour = "cyan")) +labs(title = "PILOTOS CON MÁS VICTORIAS",
    subtitle = ) #+ transition_reveal(date)

Remando a contracorriente

trulli <- readPNG("./imagenes/pilotos/trulli.png")
kovalainen <- readPNG("./imagenes/pilotos/kovalainen.png")
vettel <- readPNG("./imagenes/pilotos/vettel.png")
raikkonen <- readPNG("./imagenes/pilotos/raikkonen.png")
schumacher <- readPNG("./imagenes/pilotos/schumacher.png")
hamilton <- readPNG("./imagenes/pilotos/hamilton.png")
montoya <- readPNG("./imagenes/pilotos/montoya.png")
alonso <- readPNG("./imagenes/pilotos/alonso.png")

trulli_grob <- rasterGrob(trulli, interpolate=TRUE)
kovalainen_grob <- rasterGrob(kovalainen, interpolate=TRUE)
vettel_grob <- rasterGrob(vettel, interpolate=TRUE)
raikkonen_grob <- rasterGrob(raikkonen, interpolate=TRUE)
schumacher_grob <- rasterGrob(schumacher, interpolate=TRUE)
hamilton_grob <- rasterGrob(hamilton, interpolate=TRUE)
montoya_grob <- rasterGrob(montoya, interpolate=TRUE)
alonso_grob <- rasterGrob(alonso, interpolate=TRUE)


ggremontados <- ggplot(puestos_remont_piloto_recient, aes(x = reorder(driverRef, remontados), remontados))  + geom_bar(stat = "identity") + coord_flip() + labs(x = "Pilotos", y = "Nº de puestos remontados" ) + annotation_custom(alonso_grob, xmin= -15, xmax=17, ymin= 18, ymax=20) +
  annotation_custom(kovalainen_grob, xmin= -20, xmax=24, ymin= 18.65, ymax=20) +
  annotation_custom(montoya_grob, xmin= -26.7, xmax=33, ymin= 18, ymax=21.2) +
  annotation_custom(trulli_grob, xmin= -32, xmax=40., ymin= 18.3, ymax=19.55) +
  annotation_custom(hamilton_grob, xmin= -32, xmax=42.2, ymin= 19, ymax=21.8) +
  annotation_custom(schumacher_grob, xmin= -32, xmax=44.1, ymin= 19.2, ymax=20.5) +
  annotation_custom(raikkonen_grob, xmin= -32, xmax=46.1, ymin= 19, ymax=21) +
  annotation_custom(vettel_grob, xmin= -32, xmax=48.1, ymin= 19, ymax=21.2) +
    theme(axis.line = element_line(colour = "white"),
    axis.ticks = element_line(colour = NA),
    panel.grid.major = element_line(colour = "cyan",
        linetype = "blank"), panel.grid.minor = element_line(colour = "gray20"),
    axis.title = element_text(face = "bold",
        colour = "gray50"), axis.text = element_text(size = 11,
        colour = "cyan"), axis.text.x = element_text(colour = "cyan"),
    axis.text.y = element_text(colour = "cyan"),
    plot.title = element_text(size = 15,
        face = "bold", colour = "cyan", hjust = 0.45,
        vjust = 0.75), panel.background = element_rect(fill = "gray13",
        linetype = "solid"), plot.background = element_rect(fill = "gray13",
        colour = "cyan", linetype = "solid")) +labs(title = "MAYORES REMONTADAS EN LA HISTORIA")
  

ggremontados

Mundiales por escuderias y pilotos

En valores relativos

#datos de escuderias pa quien quiera hacer algo
#escuderias <- rio::import(file = "./datos/constructors.csv")
#escuderias2 <- rio::import(file = "./datos/constructor_standings.csv")
#result_escuderias <- rio::import(file = "./datos/constructor_results.csv")

#pilotos <- rio::import(file = "./datos/drivers.csv")
#resultados <- rio::import(file = "./datos/results.csv")
#carreras <- rio::import(file = "./datos/races.csv")
#escuderiasesp <- escuderias %>% filter(nationality == "Spanish") #escuderias españolas

#campeones_esc <- full_join(pilotos, resultados, c("driverId" = "driverId")) %>% full_join(., carreras, c("raceId" = "raceId")) %>% select(driverId, driverRef, nationality, constructorId, points, year, round) %>% full_join(., escuderias, c("constructorId" = "constructorId")) %>% select(driverId, driverRef, nationality.x, constructorId, points, year, name, round) %>%  group_by(year, driverRef) %>%  mutate(puntos_totales = cumsum(points)) %>% ungroup() %>% group_by(year) %>% slice_max(puntos_totales, n=1) %>% select(name, driverRef) %>% group_by(name, driverRef) %>% mutate(total_camp = sum( NN = n())) %>% arrange(name) 




#library(treemap)
#library(d3treeR)


# basic treemap
#gg_esc_campeones <- treemap(campeones_esc,
            #index=c("name","driverRef"),
            #vSize="total_camp",
            #type="index",
            #vColor = "name",
            #fontsize.labels=c(25,17),
            #bg.labels=c("transparent"),
            #palette = "Set2",
            #align.labels=list(
              #c("center", "center"), 
              #c("center", "bottom")),
            #title = "Escuderías con más campeones",
            #title.legend = "Escuderías")   
inter_camp <- d3tree2(gg_esc_campeones ,  rootname = "Escuderías y Campeones")
inter_camp

En valores absolutos

campeones_esc <- full_join(pilotos, resultados, c("driverId" = "driverId")) %>% full_join(., carreras, c("raceId" = "raceId")) %>% select(driverId, driverRef, nationality, constructorId, points, year, round) %>% full_join(., escuderias, c("constructorId" = "constructorId")) %>% select(driverId, driverRef, nationality.x, constructorId, points, year, name, round) %>%  group_by(year, driverRef) %>%  mutate(puntos_totales = cumsum(points)) %>% ungroup() %>% group_by(year) %>% slice_max(puntos_totales, n=1) %>% ungroup() %>% count(name) %>% arrange(desc(n))

campeones_esc$n=as.factor(campeones_esc$n)

gg_campeones_es <- ggplot(campeones_esc, aes(x=name, y=n)) +
  geom_bar(stat="identity", width=2, color="white") + 
  theme(axis.line = element_line(colour = "gray13"),
    axis.ticks = element_line(colour = "gray13"),
    panel.grid.major = element_line(colour = "gray13"),
    panel.grid.minor = element_line(colour = "gray13"),
    axis.title = element_text(family = "Bookman",
        size = 8, face = "bold"), axis.text = element_text(face = "bold",
        colour = "cyan"), axis.text.x = element_text(colour = "gray13"),
    axis.text.y = element_text(colour = "gray13"),
    plot.title = element_text(family = "Bookman",
        size = 14, face = "bold", colour = "cyan", hjust=0.5),
    legend.text = element_text(size = 9,
        face = "bold", colour = "cyan"),
    legend.title = element_text(size = 8,
        face = "bold", colour = "gray13"),
    panel.background = element_rect(fill = "gray13",
        colour = "gray13", linetype = "solid"),
    plot.background = element_rect(fill = "gray13",
        colour = "gray13", linetype = "solid"),
    legend.key = element_rect(fill = "cyan"),
    legend.background = element_rect(fill = "gray13")) +labs(title = "CAMPEONATOS POR ESCUDERIA",
    x = NULL, y = NULL)


ggplotly(gg_campeones_es)

Mundiales por pilotos y nacionalidad

id <- rownames(campeones)
campeones <- cbind(id=id, campeones)
campeones[, c(1)] <- sapply(campeones[, c(1)], as.numeric)

label_campeones <- campeones
number_of_bar <- nrow(label_campeones)

angle <- 90 - 360 * (label_campeones$id-0.5) /number_of_bar    
label_campeones$hjust <- ifelse( angle < -90, 1, 0)
label_campeones$angle <- ifelse(angle < -90, angle+180, angle)

base_campeones <- campeones %>% 
  group_by(nationality.x) %>% 
  summarise(start=min(id), end=max(id)) %>% 
  rowwise() %>% 
  mutate(title=mean(c(start, end)))

grid_campeones <- base_campeones
grid_campeones$end <- grid_campeones$end[ c( nrow(grid_campeones), 1:nrow(grid_campeones)-1)] + 1
grid_campeones$start <- grid_campeones$start - 1
grid_campeones <- grid_campeones[-1,]

gg_circ_victorias <- ggplot(campeones, aes(x=as.factor(year), y=total_campeonatos, fill=nationality.x, color = nationality.x)) + geom_bar(aes(x=as.factor(id), y=total_campeonatos, fill=nationality.x), stat="identity", alpha=0.5) +
  
  geom_segment(data=grid_campeones, aes(x = 0, y = 8, xend = 32, yend = 8), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_campeones, aes(x = 0, y = 6, xend = 32, yend = 6), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_campeones, aes(x = 0, y = 4, xend = 32, yend = 4), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_campeones, aes(x = 0, y = 2, xend = 32, yend = 2), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  
  annotate("text", x = rep(max(campeones$id),4), y = c(2, 4, 6, 8), label = c("2", "4", "6", "8") , color="white", size=3 , angle=0, fontface="bold", hjust=1) +
  
   geom_bar(aes(x=as.factor(id), y=total_campeonatos, fill=nationality.x), stat="identity", alpha=0.5) +
  ylim(-10,21) +
  theme_minimal() +
  theme(
    legend.position = "none",
    axis.text = element_blank(),
    axis.title = element_blank(),
    panel.grid = element_blank(),
    plot.margin = unit(rep(-1,4), "cm") ) +
  coord_polar() + 
  geom_text(data=label_campeones, aes(x=id, y=10, label=driverRef, hjust=hjust), color="white", fontface="bold",alpha=0.6, size=3.5, angle= label_campeones$angle, inherit.aes = FALSE ) +

  geom_segment(data=grid_campeones, aes(x = 0.70, y = -1, xend = 2.45, yend = -1), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE )  +
  geom_segment(data=grid_campeones, aes(x = 2.6, y = -1, xend = 3.55, yend = -1), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_campeones, aes(x = 3.65, y = -1, xend = 5.45, yend = -1), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_campeones, aes(x = 5.55, y = -1, xend = 7.35, yend = -1), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_campeones, aes(x = 7.5, y = -1, xend = 10.50, yend = -1), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_campeones, aes(x = 10.7, y = -1, xend = 19.20, yend = -1), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_campeones, aes(x = 19.4, y = -1, xend = 20.3, yend = -1), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_campeones, aes(x = 20.45, y = -1, xend = 23.4, yend = -1), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) + 
  geom_segment(data=grid_campeones, aes(x = 23.65, y = -1, xend = 24.35, yend = -1), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) + 
  geom_segment(data=grid_campeones, aes(x = 24.60, y = -1, xend = 27, yend = -1), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_campeones, aes(x = 27.2, y = -1, xend = 29.5, yend = -1), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_campeones, aes(x = 29.7, y = -1, xend = 30.5, yend = -1), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) + 
  geom_segment(data=grid_campeones, aes(x = 30.7, y = -1, xend = 31.5, yend = -1), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_campeones, aes(x = 31.7, y = -1, xend = 32.5, yend = -1), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_campeones, aes(x = 32.7, y = -1, xend = 33.5, yend = -1), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) + theme(legend.text = element_text(colour = "white"),
    legend.title = element_text( colour = "white"), 
     legend.background = element_rect(fill = "gray13", colour = "gray13"),
    legend.key = element_rect(fill = "gray13"),
    legend.position = "right",
    panel.background = element_rect(fill = "gray13" , colour = "gray13"),
    plot.background = element_rect(fill = "gray13" , colour = "gray13"))+labs(colour = "Nacionalidad", fill = "Nacionalidad")
#gg_circ_victorias
#las siguientes lineas de codigo se utilizan para eliminar los bordes blancos
ggsave("./plots/gg_circ_victorias.png", width = 8, height = 5)

Campeones del mundo

3.3 Temporada 2021

Campeón del mundo

Verstappen

Verstappen - Nuevo Campeón 2021

Final de infarto

salida <- image_read("./imagenes/CAMPEON/hamilton_adel.jpg") %>% image_scale(., "500")%>%  image_annotate(., "Hamilton se pone por delante", size = 20, gravity = "northwest", color = "cyan", boxcolor = "gray13")
latifi <- image_read("./imagenes/CAMPEON/latifi.jpg") %>% image_scale(., "500")%>%  image_annotate(., "Accidente de Latifi", size = 40, gravity = "northwest", color = "cyan", boxcolor = "gray13")
safety <- image_read("./imagenes/CAMPEON/safety.jpg") %>% image_scale(., "500")%>%  image_annotate(., "Sale el safety car", size = 40, gravity = "northwest", color = "cyan", boxcolor = "gray13")
rueda_a_rueda <- image_read("./imagenes/CAMPEON/ruedaarueda.jpeg") %>% image_scale(., "500")%>%  image_annotate(., "Igualdad máxima", size = 30, gravity = "northwest", color = "cyan", boxcolor = "gray13")
meta <- image_read("./imagenes/CAMPEON/verstappen_gana.jpg") %>% image_scale(., "500")%>%  image_annotate(., "Verstappen se hace con la victoria", size = 20, gravity = "northwest", color = "cyan", boxcolor = "gray13")
trofeo <- image_read("./imagenes/CAMPEON/campeon_trofeo.jpg") %>% image_scale(., "500")%>%  image_annotate(., "Nuevo campeón del mundo", size = 30, gravity = "northwest", color = "cyan", boxcolor = "gray13")
last_race <- c(salida, latifi, safety, rueda_a_rueda, meta, trofeo)

image_animate(image_scale(last_race), fps = 0.5)

Verstappen vs Hamilton

vers_vs_ham <- data.frame(
  "nombre" = c("Max Verstappen", "Lewis Hamilton"),
  "piloto" = c("./imagenes/pilotos/verstappen.jpg","./imagenes/pilotos/hamilton.png"),
  "pais" = c("./imagenes/paises/holanda.png", "./imagenes/paises/uk.png"),
  "escuderia" = c("./imagenes/escuderias/redbull.png", "./imagenes/escuderias/mercedes.png"),
  "puntos" = c(395.5, 387.5),
  "victorias" = c(10, 8),
  "podiums" = c(18, 17),
  "poles" = c(10, 5),
  "vueltas_rapidas" = c(6, 6))
  

 
tabla_ver_ham <- vers_vs_ham %>% gt() %>% 
  text_transform( locations = cells_body(columns = c(piloto)), fn = function(x) {gt::local_image(x, height = 80)}) %>% 
  text_transform( locations = cells_body(columns = c(pais)), fn = function(x) {gt::local_image(x, height = 60)}) %>%  
  text_transform( locations = cells_body(columns = c(escuderia)), fn = function(x) {gt::local_image(x, height = 80)}) %>% 
  tab_header(title = md("**Verstappen vs Hamilton**"), subtitle = md("2021")) %>%   cols_label(
    nombre = html(""),
    piloto = html(""),
    pais = html(""),
    escuderia = html("Escudería"),
    puntos = html("Puntos"),
    victorias = html("Victorias"),
    podiums = html("Podiums"),
    poles = html("Poles"),
    vueltas_rapidas = html("Vueltas rápidas")) %>%  
  tab_options(table.background.color = "gray13",   table.font.color.light = "cyan") %>% 
  cols_align(align = "center",
  columns = everything())


tabla_ver_ham
Verstappen vs Hamilton
2021
Escudería Puntos Victorias Podiums Poles Vueltas rápidas
Max Verstappen 395.5 10 18 10 6
Lewis Hamilton 387.5 8 17 5 6

Parrilla de pilotos

pilotos_2021 <- full_join(carreras, resultados, c("raceId" = "raceId") ) %>% 
  full_join(.,pilotos, c("driverId"="driverId")) %>% 
  filter(year=="2021") %>% 
  select(forename,surname, year) %>% distinct(forename,surname) %>% na.omit() %>% arrange(surname) %>% str_replace_all(., "é", "é") %>% str_replace_all(., "ä", "ä") %>% str_replace_all(., "ö", "ö")

nombres_normales <- c("Alonso", "Bottas", "Gasly", "Giovinazzi", "Hamilton", "Latifi", "Leclerc", "Mazepin", "Norris", "Ocon", "Pérez", "Räikkönen", "Ricciardo", "Russell", "Sainz", "Schumacher", "Stroll", "Tsunoda", "Verstappen", "Vettel")

pilotos_2021 <-  full_join(carreras, resultados, c("raceId" = "raceId") ) %>% 
  full_join(.,pilotos, c("driverId"="driverId")) %>% 
  filter(year=="2021") %>% 
  select(forename,surname, year) %>% distinct(forename,surname) %>% na.omit() %>% arrange(surname) %>% add_column(nombres_normales)  
 

# Pérez
# Räikkönen
fotos_pil_2021 <- c("./imagenes/pilotos/alonso.png", "./imagenes/pilotos/bottas.png", "./imagenes/pilotos/gasly.png", "./imagenes/pilotos/giovinazzi.jpg", "./imagenes/pilotos/hamilton.png", "./imagenes/pilotos/latifi.png", "./imagenes/pilotos/leclerc.png", "./imagenes/pilotos/mazepin.png", "./imagenes/pilotos/norris.png", "./imagenes/pilotos/ocon.jpg", "./imagenes/pilotos/perez.png", "./imagenes/pilotos/raikkonen.png", "./imagenes/pilotos/ricciardo.png", "./imagenes/pilotos/russell.png", "./imagenes/pilotos/sainz.png", "./imagenes/pilotos/mick.png", "./imagenes/pilotos/stroll.png", "./imagenes/pilotos/tsunoda.png", "./imagenes/pilotos/verstappen.jpg", "./imagenes/pilotos/vettel.png")

fotos_pais_2021 <- c("./imagenes/paises/espanya.png", "./imagenes/paises/finlandia.png", "./imagenes/paises/francia.png", "./imagenes/paises/italia.png","./imagenes/paises/uk.png", "./imagenes/paises/canada.png","./imagenes/paises/monaco.png", "./imagenes/paises/rusia.png","./imagenes/paises/uk.png", "./imagenes/paises/francia.png","./imagenes/paises/mexico.png", "./imagenes/paises/finlandia.png","./imagenes/paises/australia.png", "./imagenes/paises/uk.png","./imagenes/paises/espanya.png", "./imagenes/paises/alemania.png","./imagenes/paises/canada.png", "./imagenes/paises/japon.png","./imagenes/paises/holanda.png", "./imagenes/paises/alemania.png")

fotos_esc_2021 <- c("./imagenes/escuderias/alpine.png","./imagenes/escuderias/mercedes.png","./imagenes/escuderias/alphatauri.png", "./imagenes/escuderias/alfaromeo.jpg","./imagenes/escuderias/mercedes.png", "./imagenes/escuderias/williams.png","./imagenes/escuderias/ferrari.png", "./imagenes/escuderias/haas.png","./imagenes/escuderias/mclaren.png", "./imagenes/escuderias/alpine.png","./imagenes/escuderias/redbull.png", "./imagenes/escuderias/alfaromeo.jpg","./imagenes/escuderias/mclaren.png", "./imagenes/escuderias/williams.png","./imagenes/escuderias/ferrari.png", "./imagenes/escuderias/haas.png","./imagenes/escuderias/aston.png", "./imagenes/escuderias/alphatauri.png","./imagenes/escuderias/redbull.png", "./imagenes/escuderias/aston.png")

pilotos_2021 <- pilotos_2021 %>%
  add_column(fotos_pil_2021, fotos_pais_2021, fotos_esc_2021) %>% select(nombres_normales, fotos_pil_2021, fotos_pais_2021, fotos_esc_2021)


mundial_2021 <- pilotos_2021 %>% gt() %>% text_transform( locations = cells_body(columns = c(fotos_pil_2021)), fn = function(x) {gt::local_image(x, height = 100)}) %>% text_transform( locations = cells_body(columns = c(fotos_pais_2021)), fn = function(x) {gt::local_image(x, height = 50)}) %>% text_transform( locations = cells_body(columns = c(fotos_esc_2021)), fn = function(x) {gt::local_image(x, height = 70)}) %>% tab_header(title = md("**Pilotos 2021**"), subtitle = md("Parrilla")) %>%   cols_label(
    nombres_normales = html(""),
    fotos_pil_2021 = html(""),
    fotos_pais_2021 = html(""),
    fotos_esc_2021 = html("")) %>%  
  tab_options(table.background.color = "gray13",   table.font.color.light = "cyan") %>% 
  cols_align(align = "center",
  columns = everything())


#mundial_2021

Templos

globo_circ <-create_globe() %>% globe_pov(45.61560, 9.281110) %>% globe_bars(coords(lat, lng, label  = variables, color = round2), data = carreras_21_v2)  %>% scale_bars_color()
#globo_circ

3.4 El nano

Alonso vs compañeros de equipo

gc() #instruccion para que cargue el grafico, al ser tan complejo da error de no sé qué pero con esto funciona
#>            used  (Mb) gc trigger  (Mb) max used  (Mb)
#> Ncells  5029026 268.6    8721117 465.8  8721117 465.8
#> Vcells 16461297 125.6   27946008 213.3 27944246 213.2
ggalo_vs_all <- ggplot(data = ALO_VS_ALL, aes(round, puntos_acumulados, color = driverRef)) +
  geom_line() +
  geom_point() + 
  labs(title = "Alonso contra el mundo",
       subtitle = "Alonso VS cada compañero de equipo",
       y = "Puntos", x = "") + theme(axis.line = element_line(colour = "gray30",
    linetype = "solid"), axis.ticks = element_line(colour = "gray30"),
    panel.grid.major = element_line(colour = "gray20"),
    panel.grid.minor = element_line(linetype = "blank"),
    axis.title = element_text(size = 15,
        colour = "white"), axis.text = element_text(colour = "white"),
    plot.title = element_text(size = 20,
        colour = "white", hjust = 0.5), legend.text = element_text(colour = "white"),
    legend.title = element_text(colour = "white"),
    panel.background = element_rect(fill = "gray13",
        colour = "gray13", linetype = "solid"),
    plot.background = element_rect(fill = "gray13",
        colour = "gray13", linetype = "solid"),
    legend.key = element_rect(fill = "gray13",
        colour = "gray13"), legend.background = element_rect(fill = "gray13",
        colour = "gray13")) +labs(x = "Nº carrera", colour = "Piloto") + theme(plot.subtitle = element_text(size = 10,
    hjust = 0.5)) + facet_wrap( ~ year) + transition_reveal(round)

#ggalo_vs_all

Alonso vs Hamilton

#se necesita tener cargado "n_carreras_nom", "victorias_con_nombre"
#1234

alo_vs_ham_tabla <- alo_vs_ham %>% gt() %>% text_transform( locations = cells_body(columns = c(fotos_esp_ing)), fn = function(x) {gt::local_image(x, height = 50)}) %>% text_transform( locations = cells_body(columns = c(fotos_ALO_vs_HAM)), fn = function(x) {gt::local_image(x, height = 100)}) %>% tab_header(title = md("**Alonso vs Hamilton**"), subtitle = md("Comparación")) %>%   cols_label(
    driverRef = html(""),
    numero_carreras = html("Nº carreras"),
    n_victorias = html("Nº victorias"),
    fotos_esp_ing = html("País"),
    fotos_ALO_vs_HAM = html("")) %>%  
  tab_options(table.background.color = "gray13",   table.font.color.light = "cyan") %>% 
  cols_align(align = "center",
  columns = everything())

alo_vs_ham_tabla
Alonso vs Hamilton
Comparación
Nº carreras Nº victorias País
alonso 323 32
hamilton 275 98

4. Trabajos en los que te has basado

Con los datos que hemos encontrado, existen una serie de códigos que ya trabajan con estos datos, especialmente este, a partir del cual, sobre todo, nos hemos fijado en la estética de los gráficos y las imagenes con las que los acompañaba. También hemos obtenido ideas para trabajar los datos, pero en menor medida que el apartado visual, ya que el trabajo citado se centra unicamente en la temporada actual, mientras que Alt+ Formula 1 combina tanto la actualidad como la historia en general de la F1.

5. Bibliografía

La gran parte del trabajo se ha centrado en los datos obtenidos a partir de Kaggle, por lo que no son muchas las páginas auxiliares que hemos utilizado.
Para la obtención de las audiencias se han utilizado tanto esta página web , como este post de twitter.

---
title: "Alt + Formula 1"
author: "Cayetano Romero Monteagudo (caromon3@alumni.uv.es)  \n\n Alejandro García Segarra (agarse4@alumni.uv.es)  \n \n Carlos García Castilla (garcas8@alumni.uv.es). \n\n Universitat de València"
date: "Diciembre de 2021 (actualizado el `r format(Sys.time(), '%d-%m-%Y')`)"
output:
  html_document:
    #css: "./assets/my_css_file.css"
    theme: darkly
    highlight: tango 
    toc: true
    toc_depth: 3 
    toc_float: 
      collapsed: true
      smooth_scroll: true
    self_contained: true
    number_sections: false
    df_print: kable
    code_folding: hide
    code_download: true
editor_options: 
  chunk_output_type: console
---
<!--- chunk para que la fuente sea la oficial de la Formula 1 -->
```{css, echo=FALSE} 
@font-face {
  font-family: F1;
  src: url(https://www.formula1.com/etc/designs/fom-website/fonts/F1Regular/Formula1-Regular.ttf);
}

span{
  font-family: F1;
}

a{
  font-family: F1;
}

.nav-pills>li.active>a:focus {
    color: #ffffff;
    background-color: lightgray;
}

.container-fluid, .container-fluid h1 {
    font-family: F1;
    line-height: 1.7;
}

.container-fluid p {
    font-family: F1;
    color:cyan;
}

h1,h2,h3,h4,h5,h6,p, table {
  font-family: F1;
  color: cyan
}
```

```{r chunk-setup, include = FALSE}
knitr::opts_chunk$set(echo = TRUE, eval = TRUE, message = FALSE, warning = FALSE, 
                      #results = "hold",
                      cache = FALSE, cache.path = "/caches/", comment = "#>",
                      #fig.width = 7, #fig.height= 7,   
                      #out.width = 7, out.height = 7,
                      collapse = TRUE,  fig.show = "hold",
                      fig.asp = 0.628, out.width = "75%", fig.align = "center")
knitr::opts_chunk$set(dev = "png", dev.args = list(type = "cairo-png"))
```

```{r options-setup, include = FALSE}
options(scipen = 999) #- para quitar la notación científica
options("yaml.eval.expr" = TRUE) 
```


```{r klippy, echo = FALSE}
klippy::klippy(position = c("top", "right")) #- remotes::install_github("rlesur/klippy")
```



<hr class="linea-black">

<!-- El párrafo de abajo has de dejarlo casi igual, solo HAS de SUSTITUIR "perezp44" por tu usuario de Github-->
Trabajo elaborado para la asignatura "Programación y manejo de datos en la era del Big Data" de la Universitat de València durante el curso 2021-2022. El repo del trabajo está [aquí](https://github.com/cayetano108/trabajo_BigData_equipo){target="_blank"}. 

<!-- El párrafo de abajo has de dejarlo exactamente igual, NO has de cambiar nada-->

La página web de la asignatura y los trabajos de mis compañeros pueden verse [aquí](https://perezp44.github.io/intro-ds-21-22-web/07-trabajos.html){target="_blank"}.

<hr class="linea-red">



<!--Libreria de paquetes -->
```{r packages-setup, include = FALSE}
library(meme)
library(tidyverse)
library(klippy)  #- remotes::install_github("rlesur/klippy")
library(knitr)
library(rio)
library(ggplot2)
library(gganimate)
library(ggThemeAssist)
library(globe4r)  #remotes::install_github("JohnCoene/globe4r")
library(remotes) 
library(gt)
library(plotly)
library(readxl)
library(treemap) #install.packages(treemap)
library(d3treeR) #remotes::install_github("d3treeR/d3treeR")
library(magick)
library(stringr)
library(tmap)
library(flextable)
library(officer)
library(png)
library(grid)
```


# 1. Introducción

La Formula 1 es uno de los deportes/espectaculos  más importantes del mundo, logrando a lo largo de sus historia captar a millones y millones de espectadores. Nuetro trabajo pretende plasmar la grandeza de la Fórmula 1 a través de distintos datos que pueden verse a contimnuación, tanto presentes como pasados. Además, parte del trabajo se ha centrado en la figura de Fernando Alonso, el piloto con mayor importancia en España, y uno de los más relevantes en la historia de la Formula 1.  
En relación con la asignatura, la elección del tema se debe a lo fundamental que son los datos en este deporte, tanto para la elaboración de estrategias, el diseño de los coches o incluso de las pistas. Debido a todas estas razones, creemos que la F1, a pesar de no ser un tema relacionado con la economía, encaja perfectamente con la asignatura.

 <center>**THE PLAN**

```{r, eval= TRUE, echo = FALSE, warning = FALSE}

my_foto <- "https://e00-elmundo.uecdn.es/assets/multimedia/imagenes/2017/05/12/14945899346306.jpg"
meme(my_foto, "SE VIENE TRABAJAZO", "MUY TRANQUILOS", size = 2 , color = "blue", vjust = 1.05)

```




# 2. Datos

Hemos encontrado en [kaggle](https://www.kaggle.com/) bastantes conjuntos de datos con los que poder trabajar, pero especialmente [este](https://www.kaggle.com/rohanrao/formula-1-world-championship-1950-2020), que posee gran variedad de datos en lo referente a pilotos, resultados, circuitos, tiempos, etc.


## 2.1. Procesando los datos

A continuación se muestra toda la manipulación de los datos, a partir del conjunto de datos mostrado anteriormente, y que posteriormente son utilizados para la realización de los distintas partes que forman el trabajo.

```{r, eval = TRUE, echo = TRUE}

#---PREPARACION DE LOS DATOS
tiempos <- rio::import(file = "./datos/lap_times.csv")
pilotos <- rio::import(file = "./datos/drivers.csv")
resultados <- rio::import(file = "./datos/results.csv")
carreras <- rio::import(file = "./datos/races.csv")
escuderias <- rio::import(file = "./datos/constructors.csv")
escuderias2 <- rio::import(file = "./datos/constructor_standings.csv")
result_escuderias <- rio::import(file = "./datos/constructor_results.csv")
circuitos <- rio::import(file = "./datos/circuits.csv")

#----------------------------------------
#capitulo oscuro del deporte
#creo df de muertes de formula 1
muertesf1 <- data.frame(
  "orden" = 1:42,
  "driverRef" = c("Chet Miller", "Carl Scaraborough", "Onofre Marimon", "Manny Ayulo", "Bill Vukovich", "Alberto Ascari","Eugenio Castellotti", "Keith Andrews", "Pat O'Connor", "Luigi Musso", "Peter Collins", "Stuart Lewis-Evans", "Jerry Unser", "Bob Cortner", "Ivor Bueb", "Chris Bristow", "Alan Stacey", "Giulio Cabianca", "Wolfgang von Trips", "Carel Godin de Beaufort", "John Taylor", "Lorenzo Bandini", "Bob Anderson", "Jo Schlesser", "Gerhard Mitter", "Piers Courage", "Jochen Rindt", "Jo Siffert", "Roger Williamson", "François Cevert", "Peter Revson", "Helmuth Koinigg", "Mark Donohue", "Tom Pryce", "Ronnie Peterson", "Patrick Depailler", "Gilles Villeneuve", "Riccardo Paletti", "Elio de Angelis", "Roland Ratzenberger", "Ayrton Senna", "Jules Bianchi"),
  "nationality" = c("American", "American", "Argentine", "American", "American", "Italian","Italian", "American", "American", "Italian", "British", "British", "American", "American", "British", "British", "British", "Italian", "German", "Dutch", "British", "Italian" , "British", "French", "German", "British", "Austrian", "Swiss", "British", "French", "American", "Austrian", "American", "British", "Swedish", "French", "Canadian", "Italian", "Italian", "Austrian", "Brazilian", "French"),
  "fecha_muerte" = c(1953, 1953, 1954, 1955, 1955, 1955, 1957, 1957, 1958,1958,1958,1958,1959,1959,1959,1960, 1960,1961,1961,1964,1966, 1967, 1967,1968,1969, 1970,1970,1971,1973,1973,1974,1974,1975,1977,1978,1980,1982,1982,1986,1994,1994,2014))
#no pongo las comillas en los años para que se creen directamente como observaciones numericas

#creo un df con todos los años para luego fusionarlo, ya que no hay muertes todos los años 
anyos <- data.frame(
  "orden" = 1:71,
  "año" = c(1950:2020))

#sumatorio de las muertes por año
muertes_anyo <- muertesf1 %>% group_by(fecha_muerte) %>% mutate(muertesxanyo = sum(n())) %>% distinct(fecha_muerte, muertesxanyo) 

#fusiono los 2 dfs para que tenga en cuenta los años donde no hay muertes
muertesf1_final <- full_join(muertes_anyo, anyos, c("fecha_muerte" = "año")) %>% select(fecha_muerte,muertesxanyo) %>% arrange(fecha_muerte)

#convierto los N/A en 0, es decir, cuando no hay observaciones, ha habido 0 muertes
muertesf1_final[is.na(muertesf1_final)] <- 0

#------------------------------------------------
#str(resultados)
#str(pilotos)
#pilotos[, c(1)] <- sapply(pilotos[, c(1)], as.numeric)
#resultados[, c(3,6,9)] <- sapply(resultados[, c(3,6,9)], as.numeric)

#----------------------------------------------------------------------------------

#numero de carreras que ha corrido cada piloto

n_carreras <- resultados %>% group_by(driverId) %>% mutate(numero_carreras = sum(n())) %>% distinct(numero_carreras) %>% arrange(desc(numero_carreras)) 

n_carreras_nom <- full_join(n_carreras, pilotos, c ("driverId" = "driverId")) %>% select(driverId, driverRef, numero_carreras)  %>%  filter(numero_carreras >= 202 ) #los 20 qque mas carreras tienen (no funciona usar slice_max)

#--------------------------------------------------------------------------------
#nacionalidad de los pilotos a lo largo de la historia 
#mapa
nacionalidad <- pilotos %>% group_by(nationality) %>% mutate(numero_compatriotas = sum(n())) %>% distinct(numero_compatriotas) %>% arrange(desc(numero_compatriotas)) %>% 
  str_replace_all(., "British", "United Kingdom") %>% 
  str_replace_all(., "American", "United States") %>% 
  str_replace_all(., "Italian", 'Italy') %>% 
  str_replace_all(., "French" , 'France') %>% 
  str_replace_all(., "German" , 'Germany') %>% 
  str_replace_all(., "Brazilian" , 'Brazil') %>% 
  str_replace_all(., "Argentine" , 'Argentina') %>% 
  str_replace_all(., "Swiss" , 'Switzerland') %>% 
  str_replace_all(., "Belgian" , 'Belgium') %>% 
  str_replace_all(., "South African" , 'South Africa') %>% 
  str_replace_all(., "Japanese" , 'Japan') %>% 
  str_replace_all(., "Australian" , 'Australia') %>%
  str_replace_all(., "Dutch" , 'Netherlands') %>%
  str_replace_all(., "Spanish" , 'Spain') %>%
  str_replace_all(., "Austrian" , 'Austria') %>%
  str_replace_all(., "Canadian" , 'Canada') %>%
  str_replace_all(., "Swedish" , 'Sweden') %>%
  str_replace_all(., "Finnish" , 'Finland') %>%
  str_replace_all(., "New Zealander" , 'New Zealand') %>%
  str_replace_all(., "Mexican" , 'Mexico') %>%
  str_replace_all(., "Irish" , 'Ireland') %>%
  str_replace_all(., "Danish" , 'Denmark') %>%
  str_replace_all(., "Portuguese" , 'Portugal') %>%
  str_replace_all(., "Monegasque" , 'France') %>%
  str_replace_all(., "Rhodesian" , 'Zimbabwe') %>%
  str_replace_all(., "Uruguayan" , 'Uruguay') %>%
  str_replace_all(., "Russian" , 'Russia') %>%
  str_replace_all(., "Colombian" , 'Colombia') %>%
  str_replace_all(., "Venezuelan" , 'Venezuela') %>%
  str_replace_all(., "East German" , 'German') %>%
  str_replace_all(., "Indian" , 'India') %>%
  str_replace_all(., "Thai" , 'Thailand') %>%
  str_replace_all(., "Polish" , 'Poland') %>%
  str_replace_all(., "Monegasque" , 'France') %>%
  str_replace_all(., "Hungarian" , 'Hungary') %>%
  str_replace_all(., "Czech" , 'Czech Rep.') %>%
  str_replace_all(., "Malaysian" , 'Malaysia') %>%
  str_replace_all(., "Chilean" , 'Chile') %>%
  str_replace_all(., "Liechtensteiner" , 'Switzerland') %>%
  str_replace_all(., "American-Italian" , 'United States') %>%
  str_replace_all(., "Argentine-Italian" , 'Argentina') %>%
  str_replace_all(., "Indonesian" , 'Indonesia')

paises_normales <- c("United Kingdom", "United States", "Italy", "France", "Germany", "Brazil", "Argentina", "Switzerland", "Belgium", "South Africa", "Japan", "Australia", "Netherlands", "Spain", "Austria", "Canada", "Sweden", "Finland", "New Zealand", "Mexico", "Ireland", "Denmark", "Portugal", "France", "Zimbabwe", "Uruguay", "Russia", "Colombia", "Venezuela", "Germany", "India", "Thailand", "Poland", "Hungary", "Czech Rep.", "Malaysia", "Chile", "Switzerland", "United States", "Argentina", "Indonesia" )

nacionalidad <- pilotos %>% group_by(nationality) %>% 
  mutate(numero_compatriotas = sum(n())) %>% 
  distinct(numero_compatriotas) %>% 
  arrange(desc(numero_compatriotas)) %>% 
  add_column(paises_normales) %>%  
  group_by(paises_normales) %>% 
  mutate(total_pilotos = sum(numero_compatriotas)) %>%  
  distinct(paises_normales, total_pilotos)

data(World)
world <- World; rm(World)

world <- world[!(world$name %in% c('Greenland', 'Antarctica')),]

pilotos_por_paises <- full_join(nacionalidad, world, c( "paises_normales" = "name")) 

pilotos_por_paises$country <- NULL

pilotos_por_paises[is.na(pilotos_por_paises)] <- 0

gg_pilotos_por_paises <- ggplot(data = pilotos_por_paises, aes(geometry = geometry)) + geom_sf() +
       labs(title = "Pilotos por país",
            caption = "Menor intensidad de color en aquellos paises con más pilotos")

gg_pilotos_por_paises <- gg_pilotos_por_paises + 
  geom_sf(aes(fill = total_pilotos)) + 
  theme(plot.subtitle = element_text(colour = "white"),
    plot.caption = element_text(colour = "white"),
    axis.ticks = element_line(linetype = "blank"),
    panel.grid.major = element_line(linetype = "blank"),
    panel.grid.minor = element_line(linetype = "blank"),
    axis.text = element_text(colour = "gray13"),
    plot.title = element_text(size = 20,
        colour = "white", hjust = 0.5), legend.text = element_text(colour = "white"),
    legend.title = element_text(colour = "white"),
    panel.background = element_rect(fill = "gray13"),
    plot.background = element_rect(fill = "gray13"),
    legend.background = element_rect(fill = "gray13")) +labs(fill = "Nª de pilotos") + theme(panel.background = element_rect(colour = "gray13"),
    plot.background = element_rect(colour = "gray13")) + theme(panel.grid.major = element_line(colour = NA),
    panel.background = element_rect(linetype = "solid"),
    plot.background = element_rect(linetype = "solid")) + scale_fill_gradient2(low = "white", mid = "pink", high = "red", midpoint = .02)

#------------------------------------------------------------------------------
# numero de victorias por piloto
victorias <- resultados %>% filter(position == "1") %>% 
  group_by(driverId) %>% 
  mutate(n_victorias = sum(n())) %>% 
  distinct(n_victorias) %>% 
  arrange(desc(n_victorias))

#aqui fusiono con el df de pilotos para que aparezca el nombre y no sólo el ID del piloto en cuestion, y hago lo mismo que en el apartado de arriba para ordenar
victorias_con_nombre <- full_join(victorias, pilotos, c ("driverId" = "driverId")) %>% 
  select(driverId, nationality, driverRef, n_victorias)  
mas_victorias <- victorias_con_nombre %>%  filter(n_victorias >= 25 ) 

#victorias 2
victorias2 <- full_join(pilotos, resultados, c("driverId" = "driverId")) %>% 
  full_join(., carreras, c("raceId" = "raceId")) %>% 
  select(driverId, driverRef, position, year, date) %>% 
  filter(position == 1) %>% 
  group_by(driverRef) %>% 
  mutate(victoria_num = sum( NN = n())) %>% 
  filter(victoria_num >= 20) %>% 
  arrange(victoria_num, date) %>% 
  mutate( suma_vic = cumsum(position))

#-------------------------------------------------

#resultado medio

resultados[, c(7)] <- sapply(resultados[, c(7)], as.numeric)
resultados[is.na(resultados)] <- 25 

#resultado_medio <-  full_join(pilotos, resultados,  c ("driverId" = "driverId")) %>% 
#select(driverId, driverRef, position) %>% 
 # group_by(driverId) %>% 
  #mutate(result_medio = mean(position)) %>% 
  #distinct (driverId, driverRef, result_medio) %>% arrange(result_medio)

#resultado medio en clasificacion
resultado_medio_clas <-  full_join(pilotos, resultados,  c ("driverId" = "driverId")) %>% 
  select(driverId, driverRef, grid) %>% 
  group_by(driverId) %>% 
  mutate(result_medio_clas = mean(grid)) %>% 
  distinct (driverId, driverRef, result_medio_clas)  %>% 
  filter(result_medio_clas > 0) %>% 
  arrange(result_medio_clas)  


#numero de vueltas liderando


#puntos por carrera (puntos/carrera)

puntos_x_carrera <-  full_join(pilotos, resultados,  c ("driverId" = "driverId")) %>% select(driverId, driverRef, points) %>% full_join(., n_carreras,  c ("driverId" = "driverId")) %>% group_by(driverId) %>% mutate(total_puntos = sum(points)) %>% distinct(driverId, driverRef, numero_carreras, total_puntos) %>% mutate(media_puntos = total_puntos/numero_carreras) %>% arrange(desc(media_puntos))

#-------------------------------
#remontadas

#mas posiciones remontadas en una carrera gran premio


resultados[, c(6,9)] <- sapply(resultados[, c(6,9)], as.numeric) #transformo variables grid y positionOrder en numerico
str(resultados) # para comprobarlo


#mayores remontadas de la historia, se resta posicion de salida - posicion final
puestos_remontados <- resultados %>% mutate(remontados = grid - positionOrder) %>% 
  select(raceId, driverId, grid, positionOrder, remontados) 


#de toda la historia
circuitos_gp <- full_join(carreras, circuitos, c("circuitId" = "circuitId")) %>% 
  select(circuitId, name.y, raceId, year)

ptos_remont_carrera <- inner_join(puestos_remontados, circuitos_gp)

puestos_remont_piloto <- full_join(pilotos, ptos_remont_carrera, c("driverId" = "driverId")) %>% 
  slice_max(remontados, n=10) %>% 
  select(driverId, driverRef,name.y,year, raceId, grid, positionOrder, remontados)

# de la hisotoria reciente
circuitos_gp_recient <- full_join(carreras, circuitos, c("circuitId" = "circuitId")) %>% 
  select(circuitId, name.y, raceId, year) %>% 
  filter(year >= 1995)

ptos_remont_carrera_recient <- inner_join(puestos_remontados, circuitos_gp_recient)

puestos_remont_piloto_recient <- full_join(pilotos, ptos_remont_carrera_recient, c("driverId" = "driverId")) %>% 
  slice_max(remontados, n=10) %>% 
  select(driverId, driverRef, name.y, year,raceId, grid, positionOrder, remontados) %>% 
  slice(1:4,6:8,10) %>% 
  arrange(desc(remontados))


#--------------------------------
#mundiales por naionalidad y piloto
campeones <- full_join(pilotos, resultados, c("driverId" = "driverId")) %>% 
  full_join(., carreras, c("raceId" = "raceId")) %>% 
  select(driverId, driverRef, nationality, constructorId, points, year, round) %>% 
  full_join(., escuderias, c("constructorId" = "constructorId")) %>% 
  select(driverId, driverRef, nationality.x, constructorId, points, year, name, round) %>%  
  group_by(year, driverRef) %>%  
  mutate(puntos_totales = cumsum(points)) %>% 
  ungroup() %>% 
  group_by(year) %>% 
  slice_max(puntos_totales, n=1) %>% 
  ungroup() %>% group_by(driverRef) %>% 
  mutate(total_campeonatos = sum(NN = n())) %>% 
  distinct(driverRef, nationality.x, total_campeonatos) %>% 
  arrange(nationality.x, total_campeonatos)

#----------------------------------
#temporada 2021 tabla

pilotos_2021 <- full_join(carreras, resultados, c("raceId" = "raceId") ) %>% 
  full_join(.,pilotos, c("driverId"="driverId")) %>% 
  filter(year=="2021") %>% 
  select(forename,surname, year) %>% 
  distinct(forename,surname) %>% 
  na.omit() %>% 
  arrange(surname) %>% 
  str_replace_all(., "Ã©", "é") %>% 
  str_replace_all(., "Ã¤", "ä") %>% 
  str_replace_all(., "Ã¶", "ö")

nombres_normales <- c("Alonso", "Bottas", "Gasly", "Giovinazzi", "Hamilton", "Latifi", "Leclerc", "Mazepin", "Norris", "Ocon", "Pérez", "Räikkönen", "Ricciardo", "Russell", "Sainz", "Schumacher", "Stroll", "Tsunoda", "Verstappen", "Vettel")

pilotos_2021 <-  full_join(carreras, resultados, c("raceId" = "raceId") ) %>% 
  full_join(.,pilotos, c("driverId"="driverId")) %>% 
  filter(year=="2021") %>% 
  select(forename,surname, year) %>% 
  distinct(forename,surname) %>% 
  na.omit() %>% 
  arrange(surname) %>% 
  add_column(nombres_normales) 

#--------------------------
#temporada 2021 templos

carreras_21 <- full_join(carreras,circuitos, c("circuitId" = "circuitId")) %>%
  filter(year=="2021") %>%
  select(round, name.x, name.y, date, location,country, lat, lng, alt) %>%
  arrange(round) %>% 
  mutate(round2 = round) 

carreras_21_v2 <- carreras_21[, c(1, 4, 10, 2, 3, 5, 6, 7, 8, 9)]
carreras_21_v2 <- carreras_21_v2%>%  unite(. ,variables, c(1, 5, 7), sep = "; ")


#----------------------------------
#el nano contra todos


alovsall <- full_join(pilotos, resultados, c ("driverId" = "driverId")) %>%  
  select(driverRef, resultId, raceId, constructorId, position, points) %>% 
  full_join(., escuderias, c ("constructorId" = "constructorId")) %>% 
  select(driverRef, resultId, raceId, constructorId, position, position, points, name) %>% 
  full_join(., carreras, c ("raceId" = "raceId")) %>% 
  select(driverRef, resultId, raceId, constructorId, position, position, points, name.x, year, round)

#alo_vs_marques <- alovsall %>% filter(year == 2001, driverRef %in% c("alonso", "marques"), round <= 14)

alo_vs_trulli <- alovsall %>% filter(year %in% c(2003, 2004), driverRef %in% c("alonso", "trulli")) %>% slice(1:15, 17:67) %>% group_by(driverRef, year) %>% mutate(puntos_acumulados = cumsum(points)) %>% ungroup()

alo_vs_fisichella <- alovsall %>% filter(year %in% c(2005, 2006), driverRef %in% c("alonso", "fisichella"))  %>% group_by(driverRef, year) %>% mutate(puntos_acumulados = cumsum(points)) %>% ungroup()

alo_vs_hamilton <- alovsall %>% filter(year %in% c(2007) ,driverRef %in% c("alonso", "hamilton"))  %>% group_by(driverRef, year) %>% mutate(puntos_acumulados = cumsum(points)) %>% ungroup()

alo_vs_piquet <- alovsall %>% filter(year %in% c(2008, 2009), driverRef %in% c("alonso", "piquet_jr")) %>% slice(1:28, 36:63) %>% group_by(driverRef, year) %>% mutate(puntos_acumulados = cumsum(points)) %>% ungroup()

#alo_vs_grosjean <- alovsall %>% filter(year == 2009, driverRef %in% c("alonso", "grosjean"), round >= 11)

alo_vs_massa <- alovsall %>% filter(year %in% c(2010, 2011, 2013), driverRef %in% c("alonso", "massa")) %>% group_by(driverRef, year) %>% mutate(puntos_acumulados = cumsum(points)) %>% ungroup()

alo_vs_raikkonen <- alovsall %>% filter(year == 2014, driverRef %in% c("alonso", "raikkonen")) %>% group_by(driverRef, year) %>% mutate(puntos_acumulados = cumsum(points)) %>% ungroup()

alo_vs_button <- alovsall %>% filter(year %in% c(2015, 2016), driverRef %in% c("alonso", "button")) %>% group_by(driverRef, year) %>% mutate(puntos_acumulados = cumsum(points)) %>% ungroup()

alo_vs_vandoorne <- alovsall %>% filter(year %in% c(2017, 2018), driverRef %in% c("alonso", "vandoorne")) %>% slice(1:45, 47:81) %>% group_by(driverRef, year) %>% mutate(puntos_acumulados = cumsum(points))%>% ungroup() 

alo_vs_ocon <- alovsall %>% filter(year == 2021, driverRef %in% c("alonso", "ocon")) %>% group_by(driverRef, year) %>% mutate(puntos_acumulados = cumsum(points)) %>% ungroup()

ALO_VS_ALL <- full_join(alo_vs_trulli, alo_vs_fisichella, c("driverRef"= "driverRef", "resultId" = "resultId", "raceId" = "raceId", "constructorId" = "constructorId", "position" = "position", "points" = "points", "name.x" = "name.x", "year" = "year", "round" = "round" , "puntos_acumulados" = "puntos_acumulados")) %>% 
  full_join(., alo_vs_hamilton, c("driverRef"= "driverRef", "resultId" = "resultId", "raceId" = "raceId", "constructorId" = "constructorId", "position" = "position", "points" = "points", "name.x" = "name.x", "year" = "year", "round" = "round", "puntos_acumulados" = "puntos_acumulados")) %>% 
  full_join(., alo_vs_piquet, c("driverRef"= "driverRef", "resultId" = "resultId", "raceId" = "raceId", "constructorId" = "constructorId", "position" = "position", "points" = "points", "name.x" = "name.x", "year" = "year", "round" = "round", "puntos_acumulados" = "puntos_acumulados")) %>% 
  full_join(., alo_vs_massa, c("driverRef"= "driverRef", "resultId" = "resultId", "raceId" = "raceId", "constructorId" = "constructorId", "position" = "position", "points" = "points", "name.x" = "name.x", "year" = "year", "round" = "round", "puntos_acumulados" = "puntos_acumulados"))  %>% 
  full_join(., alo_vs_raikkonen, c("driverRef"= "driverRef", "resultId" = "resultId", "raceId" = "raceId", "constructorId" = "constructorId", "position" = "position", "points" = "points", "name.x" = "name.x", "year" = "year", "round" = "round", "puntos_acumulados" = "puntos_acumulados")) %>% 
  full_join(., alo_vs_button, c("driverRef"= "driverRef", "resultId" = "resultId", "raceId" = "raceId", "constructorId" = "constructorId", "position" = "position", "points" = "points", "name.x" = "name.x", "year" = "year", "round" = "round", "puntos_acumulados" = "puntos_acumulados")) %>% 
  full_join(., alo_vs_vandoorne, c("driverRef"= "driverRef", "resultId" = "resultId", "raceId" = "raceId", "constructorId" = "constructorId", "position" = "position", "points" = "points", "name.x" = "name.x", "year" = "year", "round" = "round", "puntos_acumulados" = "puntos_acumulados")) %>% 
  full_join(., alo_vs_ocon, c("driverRef"= "driverRef", "resultId" = "resultId", "raceId" = "raceId", "constructorId" = "constructorId", "position" = "position", "points" = "points", "name.x" = "name.x", "year" = "year", "round" = "round", "puntos_acumulados" = "puntos_acumulados"))


#------------------------------
#el nano contra hamilton
fotos_ALO_vs_HAM <- c("./imagenes/pilotos/alonso.png", "./imagenes/pilotos/hamilton.png")
fotos_esp_ing <- c("./imagenes/paises/espanya.png", "./imagenes/paises/uk.png")
n_carreras_alo_ham <- n_carreras_nom %>% filter(driverRef %in% c("alonso", "hamilton"))

n_victorias_alo_ham <- victorias_con_nombre %>% filter(driverRef %in% c("alonso", "hamilton"))

alo_vs_ham <- full_join(n_carreras_alo_ham, n_victorias_alo_ham, c("driverRef"= "driverRef")) %>% select( driverRef, numero_carreras, n_victorias) %>% add_column(fotos_esp_ing, fotos_ALO_vs_HAM) 
```


# 3. The plan

## 3.1 Un paseo por la historia 
El nacimiento de la Fórmula 1 se remonta a 1950, donde la FIA, Federación Internacional del Automóvil, decidió unir varios grandes premios de diferentes países, para crear el ampeonato undial de pilotos. El primer gran premio se celebró en el circuito de Silverstone, el 13 de mayo de 1950.  Desde entonces, La Fórmula 1 ha ido evolucionando, tanto a nivel de coches, circuitos, o logos, para acabar siendo el espectáculo que es en la actualidad.

### Coches
El automóvil puede ser el elemento que mayores cambios ha experimetnado con el paso del tiempo. En los años 50, las escuderias más destacables eran los propios fabricantes de vehículos, por ejemplo Maserati o Alfa Romeo. Los coches en esta década se caracterizaban por tener un motor frontal, a diferencia de lo que pasaría en años posteriores.    

A partir de la década de los 60, se comenzaron a utilizar vehículos con motor central, suponiendo una importante mejora en el desempeño de los coches. Además, se dio peso a otros aspectos, como la composición del chasis, siendo la escudería lotus pionera en introducir los chasis monocasco de aluminio.   

En los 70, la aerodinámica pasó a tener un papel fundamental, introduciendose el efecto suelo, q permitía rodar a mayor velocidad en las curvas. Entre 1980 y 1990 destacan las ayudas electrónicas, como por ejemplo la suspensión activa, el cambio de marchas semi-automático, o el control de tracción.    

Desde entonces, hasta la actualidad, los cammbios en los coches no han sido tan relevantes, produciendose un equilibrio en las mejoras de las distintas partes del vehículo: aerodinámica, chasis y motor.
```{r}

coche1950 <- image_read("./imagenes/coches/1950.jpg") %>% image_scale(., "500") %>%  image_annotate(., "1950", size = 40, gravity = "southwest", color = "white")
coche1960 <- image_read("./imagenes/coches/1960.jpg") %>% image_scale(., "500")%>%  image_annotate(., "1960", size = 40, gravity = "southwest", color = "white")
coche1970 <- image_read("./imagenes/coches/1970.jpg") %>% image_scale(., "500")%>%  image_annotate(., "1970", size = 40, gravity = "southwest", color = "white")
coche1980 <- image_read("./imagenes/coches/1980.jpg") %>% image_scale(., "500")%>%  image_annotate(., "1980", size = 40, gravity = "southwest", color = "white")
coche1990 <- image_read("./imagenes/coches/1990.jpg") %>% image_scale(., "500")%>%  image_annotate(., "1990", size = 40, gravity = "southwest", color = "white")
coche2005 <- image_read("./imagenes/coches/2005.jpg") %>% image_scale(., "500")%>%  image_annotate(., "2005", size = 40, gravity = "southwest", color = "white")
coche2020 <- image_read("./imagenes/coches/2020.jpg") %>% image_scale(., "500")%>%  image_annotate(., "2020", size = 40, gravity = "southwest", color = "white")
coches <- c(coche1950, coche1960, coche1970, coche1980, coche1990, coche2005, coche2020)

image_animate(image_scale(coches), fps = 0.5)
```

### Logos  {.tabset}

#### Logo de 1985 a 1986

Aunque no tan espectacular, también se ha producido ua evolución de la estética del logo, pasando de una imagen más cargada, en la que aparece hasta el símbolo de la FIA, al logo actual, compuesto por la F y el 1.

<center>
![Logo de 1950 a 1986](./imagenes/logos/1985-1986.png){width=400 height=70}
</center>


#### Logo de 1987 a 1993
<center>
![Logo de 1987 a 1993](./imagenes/logos/1987-1993.jpg){width=250 height=300}
</center>


#### Logo de 1994 a 2017
<center>
![Logo de 1994 a 2017](./imagenes/logos/1994-2017.png){width=450 height=300}
</center>


#### Logo Actual
<center>
![LOGO desde 2018 hasta la actualidad](./imagenes/logos/2018-.png){width=400 height=300}
</center>  


### Capítulo oscuro del deporte

Hay que ser conscientes que, de igual manera que la Formula 1 es espectacular, sus pilotos se exponen a un gran riesgo, debido a las velocidades que alcanzan. Prueba de ello es, el gráfico mostrado a continuación, en el cual se refleja todas las muertes de pilotos producidas en la F1. El  total es de 42, siendo la peor decada los años 50, produciendose 15 fallecimientos.       

Sin embargo, la tendencia ha sido claramente negativa, debido a las diferentes medidas de seguridad que se han ido incorporando. La última introducción ha sido el halo, centrado en evitar golpes o aplastamientos de la cabeza del piloto. Otras mejoras destacadas han sido, las proteciones y grava en los circuitos, los monos ignífugos o la reglamentación en el diseño del casco.

```{r, eval = TRUE, echo = TRUE}
gg_muertes <- ggplot(muertesf1_final, aes(x = fecha_muerte, y = muertesxanyo )) +  geom_bar(stat = "identity", fill = "white", colour = "white") + geom_smooth(colour = "cyan", se = FALSE) + labs(x = "Año" , y = "Número de muertes")  + theme(axis.line = element_line(colour = "white"),
    axis.ticks = element_line(colour = "white"),
    panel.grid.major = element_line(colour = "gray13"),
    panel.grid.minor = element_line(colour = "gray13"),
    axis.title = element_text(colour = "white"),
    axis.text = element_text(colour = "white"),
    plot.title = element_text(colour = "white"),
    panel.background = element_rect(fill = "gray13",
        colour = "white"), plot.background = element_rect(fill = "gray13")) +labs(colour = "white") + theme(panel.grid.major = element_line(colour = "gray38",
    linetype = "dotted"), panel.grid.minor = element_line(colour = NA),
    plot.title = element_text(size = 25,
        hjust = 0.5)) +labs(title = "Accidentes mortales por año") + geom_text(data = data.frame(x = 2004.10522642875, y = 0.237450516942241, 
    label = "Tendencia negativa"), mapping = aes(x = x, y = y, 
    label = label), colour = "cyan", inherit.aes = FALSE, size = 3)

ggplotly(gg_muertes)


```


## 3.2 Lo más destacado 


### Audencias
```{r, eval = TRUE, echo = TRUE}
audiencias <- rio::import(file = "./datos/audienciasF1.csv")


gg_audiencias <- ggplot(audiencias, aes(x=year, y= numero_espectadores)) +
  geom_area() +
  geom_point( size=1.5, color="cyan", fill=alpha("cyan", 8), shape=21, stroke=2) +  
  geom_line(color="#69b3a2", size=1) +
  scale_x_continuous(
    breaks = seq(2004, 2020, 1),
    limits = c(2003, 2021)) + labs(x = "Año", y = "Numero de espectadores" )  + theme(panel.background = element_rect(fill = "gray13"),
    plot.background = element_rect(fill = "gray13")) + theme(axis.line = element_line(colour = "white"),
    panel.grid.major = element_line(colour = "gray20"),
    panel.grid.minor = element_line(colour = "gray20"),
    axis.text = element_text(colour = "white"),
    legend.position = "none") + theme(axis.title = element_text(colour = "white"),
    plot.title = element_text(colour = "white",
        hjust = 0.5)) +labs(title = "Evolución de la audiencia",
    colour = "white") + theme(axis.text.x = element_text(size = 4))


ggplotly(gg_audiencias)

```


### Presupuestos

```{r, eval = TRUE, fig.align='center', out.extra='angle=90', echo = TRUE}

presupuestos <- read_excel("datos/presupuestos.xlsx")

gg_presup <- ggplot(presupuestos, aes(year, Presupuesto, color = Escuderia)) + 
  geom_point() + geom_line() + 
  labs(x = "Año", y = "Presupuesto en €" ) +
    scale_x_continuous(
    breaks = seq(2015, 2023, 1),
    limits = c(2014, 2024)) + 
  scale_y_continuous( breaks = seq(0, 700000000, 100000000),
    limits = c(0, 600000000))  + theme(axis.ticks = element_line(colour = "white"),
    panel.grid.major = element_line(colour = "white",
        linetype = "blank"), panel.grid.minor = element_line(colour = "white",
        linetype = "blank"), axis.title = element_text(size = 14,
        face = "bold", colour = "cyan", vjust = 0.75),
    axis.text = element_text(colour = "white"),
    plot.title = element_text(size = 16,
        face = "bold", colour = "cyan", hjust = 0.5,
        vjust = 0.75), legend.text = element_text(face = "bold",
        colour = "cyan"), legend.title = element_text(size = 13,
        face = "bold", colour = "cyan"),
    panel.background = element_rect(fill = "gray13",
        colour = "white"), plot.background = element_rect(fill = "gray13"),
    legend.key = element_rect(fill = "gray13"),
    legend.background = element_rect(fill = "gray13")) +labs(title = "PRESUPUESTO DE CADA EQUIPO POR TEMPORADA") + theme(panel.grid.major = element_line(colour = NA),
    panel.grid.minor = element_line(colour = NA),
    axis.title = element_text(size = 11),
    plot.title = element_text(size = 14),
    legend.text = element_text(size = 9),
    legend.title = element_text(size = 11),
    panel.background = element_rect(fill = "gray13",
        colour = NA), plot.background = element_rect(fill = "gray13",
        colour = NA)) + theme(legend.key = element_rect(fill = "gray13"),
    legend.background = element_rect(fill = "gray13")) + theme(axis.line = element_line(colour = "gray20",
    linetype = "solid"), panel.grid.major = element_line(colour = "gray15",
    linetype = "solid"))
 
ggplotly(gg_presup) #para que sea interactivo
```


### Nacionalidades {.tabset}

#### Mapa
```{r}

ggplotly(gg_pilotos_por_paises)

```

#### Tabla 

```{r, eval = TRUE, echo = TRUE}

set_flextable_defaults(
  font.size = 15,
  font.color = "white",
  table.layout = "fixed", 
  background.color = "gray13")

flex_piloto <- flextable(nacionalidad)

small_border = fp_border(color="gray50", width = 2)

flex_piloto1 <- border_outer(flex_piloto, part="all", border = small_border )


theme_vader(flex_piloto1)
```


### Pilotos con más victorias
```{r, eval = TRUE, echo = TRUE}

ggplot(victorias2, aes(x = reorder(driverRef,suma_vic), suma_vic)) + 
  geom_line(size=5, colour = "cyan") +
  coord_flip() +
  theme(plot.subtitle = element_text(hjust = 0.5),
    plot.caption = element_text(size = 15,
        colour = "white", hjust = 0), axis.line = element_line(colour = "gray20",
        linetype = "solid"), panel.grid.major = element_line(linetype = "blank"),
    panel.grid.minor = element_line(linetype = "blank"),
    axis.title = element_text(colour = "cyan"),
    axis.text = element_text(colour = "cyan"),
    axis.text.x = element_text(colour = "white"),
    axis.text.y = element_text(colour = "white"),
    plot.title = element_text(size = 17,
        colour = "white", hjust = 0.5), panel.background = element_rect(fill = "gray13",
        colour = "gray13", linetype = "solid"),
    plot.background = element_rect(fill = "gray13",
        colour = "gray13", linetype = "solid")) +labs(title = 'Pilotos con más victorias' ,x = "Pilotos",
    y = "Nº victorias") + theme(plot.subtitle = element_text(colour = "gray50"),
    plot.title = element_text(face = "bold",
        colour = "cyan")) +labs(title = "PILOTOS CON MÁS VICTORIAS",
    subtitle = ) #+ transition_reveal(date)


```


### Remando a contracorriente
```{r, eval = TRUE, echo = TRUE}
trulli <- readPNG("./imagenes/pilotos/trulli.png")
kovalainen <- readPNG("./imagenes/pilotos/kovalainen.png")
vettel <- readPNG("./imagenes/pilotos/vettel.png")
raikkonen <- readPNG("./imagenes/pilotos/raikkonen.png")
schumacher <- readPNG("./imagenes/pilotos/schumacher.png")
hamilton <- readPNG("./imagenes/pilotos/hamilton.png")
montoya <- readPNG("./imagenes/pilotos/montoya.png")
alonso <- readPNG("./imagenes/pilotos/alonso.png")

trulli_grob <- rasterGrob(trulli, interpolate=TRUE)
kovalainen_grob <- rasterGrob(kovalainen, interpolate=TRUE)
vettel_grob <- rasterGrob(vettel, interpolate=TRUE)
raikkonen_grob <- rasterGrob(raikkonen, interpolate=TRUE)
schumacher_grob <- rasterGrob(schumacher, interpolate=TRUE)
hamilton_grob <- rasterGrob(hamilton, interpolate=TRUE)
montoya_grob <- rasterGrob(montoya, interpolate=TRUE)
alonso_grob <- rasterGrob(alonso, interpolate=TRUE)


ggremontados <- ggplot(puestos_remont_piloto_recient, aes(x = reorder(driverRef, remontados), remontados))  + geom_bar(stat = "identity") + coord_flip() + labs(x = "Pilotos", y = "Nº de puestos remontados" ) + annotation_custom(alonso_grob, xmin= -15, xmax=17, ymin= 18, ymax=20) +
  annotation_custom(kovalainen_grob, xmin= -20, xmax=24, ymin= 18.65, ymax=20) +
  annotation_custom(montoya_grob, xmin= -26.7, xmax=33, ymin= 18, ymax=21.2) +
  annotation_custom(trulli_grob, xmin= -32, xmax=40., ymin= 18.3, ymax=19.55) +
  annotation_custom(hamilton_grob, xmin= -32, xmax=42.2, ymin= 19, ymax=21.8) +
  annotation_custom(schumacher_grob, xmin= -32, xmax=44.1, ymin= 19.2, ymax=20.5) +
  annotation_custom(raikkonen_grob, xmin= -32, xmax=46.1, ymin= 19, ymax=21) +
  annotation_custom(vettel_grob, xmin= -32, xmax=48.1, ymin= 19, ymax=21.2) +
    theme(axis.line = element_line(colour = "white"),
    axis.ticks = element_line(colour = NA),
    panel.grid.major = element_line(colour = "cyan",
        linetype = "blank"), panel.grid.minor = element_line(colour = "gray20"),
    axis.title = element_text(face = "bold",
        colour = "gray50"), axis.text = element_text(size = 11,
        colour = "cyan"), axis.text.x = element_text(colour = "cyan"),
    axis.text.y = element_text(colour = "cyan"),
    plot.title = element_text(size = 15,
        face = "bold", colour = "cyan", hjust = 0.45,
        vjust = 0.75), panel.background = element_rect(fill = "gray13",
        linetype = "solid"), plot.background = element_rect(fill = "gray13",
        colour = "cyan", linetype = "solid")) +labs(title = "MAYORES REMONTADAS EN LA HISTORIA")
  

ggremontados

```

### Mundiales por escuderias y pilotos {.tabset}

#### En valores relativos
```{r, eval = TRUE, echo = TRUE, include = FALSE}
#datos de escuderias pa quien quiera hacer algo
escuderias <- rio::import(file = "./datos/constructors.csv")
escuderias2 <- rio::import(file = "./datos/constructor_standings.csv")
result_escuderias <- rio::import(file = "./datos/constructor_results.csv")

pilotos <- rio::import(file = "./datos/drivers.csv")
resultados <- rio::import(file = "./datos/results.csv")
carreras <- rio::import(file = "./datos/races.csv")
#escuderiasesp <- escuderias %>% filter(nationality == "Spanish") #escuderias españolas

campeones_esc <- full_join(pilotos, resultados, c("driverId" = "driverId")) %>% full_join(., carreras, c("raceId" = "raceId")) %>% select(driverId, driverRef, nationality, constructorId, points, year, round) %>% full_join(., escuderias, c("constructorId" = "constructorId")) %>% select(driverId, driverRef, nationality.x, constructorId, points, year, name, round) %>%  group_by(year, driverRef) %>%  mutate(puntos_totales = cumsum(points)) %>% ungroup() %>% group_by(year) %>% slice_max(puntos_totales, n=1) %>% select(name, driverRef) %>% group_by(name, driverRef) %>% mutate(total_camp = sum( NN = n())) %>% arrange(name) 




library(treemap)
library(d3treeR)


# basic treemap
gg_esc_campeones <- treemap(campeones_esc,
            index=c("name","driverRef"),
            vSize="total_camp",
            type="index",
            vColor = "name",
            fontsize.labels=c(15,20),
            bg.labels=c("transparent"),
            palette = "Set2",
            align.labels=list(
              c("center", "center"), 
              c("center", "bottom")),
            title = "Escuderías con más campeones",
            title.legend = "Escuderías")   

```


```{r, eval = TRUE, echo = TRUE}
#datos de escuderias pa quien quiera hacer algo
#escuderias <- rio::import(file = "./datos/constructors.csv")
#escuderias2 <- rio::import(file = "./datos/constructor_standings.csv")
#result_escuderias <- rio::import(file = "./datos/constructor_results.csv")

#pilotos <- rio::import(file = "./datos/drivers.csv")
#resultados <- rio::import(file = "./datos/results.csv")
#carreras <- rio::import(file = "./datos/races.csv")
#escuderiasesp <- escuderias %>% filter(nationality == "Spanish") #escuderias españolas

#campeones_esc <- full_join(pilotos, resultados, c("driverId" = "driverId")) %>% full_join(., carreras, c("raceId" = "raceId")) %>% select(driverId, driverRef, nationality, constructorId, points, year, round) %>% full_join(., escuderias, c("constructorId" = "constructorId")) %>% select(driverId, driverRef, nationality.x, constructorId, points, year, name, round) %>%  group_by(year, driverRef) %>%  mutate(puntos_totales = cumsum(points)) %>% ungroup() %>% group_by(year) %>% slice_max(puntos_totales, n=1) %>% select(name, driverRef) %>% group_by(name, driverRef) %>% mutate(total_camp = sum( NN = n())) %>% arrange(name) 




#library(treemap)
#library(d3treeR)


# basic treemap
#gg_esc_campeones <- treemap(campeones_esc,
            #index=c("name","driverRef"),
            #vSize="total_camp",
            #type="index",
            #vColor = "name",
            #fontsize.labels=c(25,17),
            #bg.labels=c("transparent"),
            #palette = "Set2",
            #align.labels=list(
              #c("center", "center"), 
              #c("center", "bottom")),
            #title = "Escuderías con más campeones",
            #title.legend = "Escuderías")   

```

```{r, fig.align='center'}

inter_camp <- d3tree2(gg_esc_campeones ,  rootname = "Escuderías y Campeones")
inter_camp


```


#### En valores absolutos

```{r}
campeones_esc <- full_join(pilotos, resultados, c("driverId" = "driverId")) %>% full_join(., carreras, c("raceId" = "raceId")) %>% select(driverId, driverRef, nationality, constructorId, points, year, round) %>% full_join(., escuderias, c("constructorId" = "constructorId")) %>% select(driverId, driverRef, nationality.x, constructorId, points, year, name, round) %>%  group_by(year, driverRef) %>%  mutate(puntos_totales = cumsum(points)) %>% ungroup() %>% group_by(year) %>% slice_max(puntos_totales, n=1) %>% ungroup() %>% count(name) %>% arrange(desc(n))

campeones_esc$n=as.factor(campeones_esc$n)

gg_campeones_es <- ggplot(campeones_esc, aes(x=name, y=n)) +
  geom_bar(stat="identity", width=2, color="white") + 
  theme(axis.line = element_line(colour = "gray13"),
    axis.ticks = element_line(colour = "gray13"),
    panel.grid.major = element_line(colour = "gray13"),
    panel.grid.minor = element_line(colour = "gray13"),
    axis.title = element_text(family = "Bookman",
        size = 8, face = "bold"), axis.text = element_text(face = "bold",
        colour = "cyan"), axis.text.x = element_text(colour = "gray13"),
    axis.text.y = element_text(colour = "gray13"),
    plot.title = element_text(family = "Bookman",
        size = 14, face = "bold", colour = "cyan", hjust=0.5),
    legend.text = element_text(size = 9,
        face = "bold", colour = "cyan"),
    legend.title = element_text(size = 8,
        face = "bold", colour = "gray13"),
    panel.background = element_rect(fill = "gray13",
        colour = "gray13", linetype = "solid"),
    plot.background = element_rect(fill = "gray13",
        colour = "gray13", linetype = "solid"),
    legend.key = element_rect(fill = "cyan"),
    legend.background = element_rect(fill = "gray13")) +labs(title = "CAMPEONATOS POR ESCUDERIA",
    x = NULL, y = NULL)


ggplotly(gg_campeones_es)

```


  
### Mundiales por pilotos y nacionalidad  

```{r, eval = TRUE, echo = TRUE}


id <- rownames(campeones)
campeones <- cbind(id=id, campeones)
campeones[, c(1)] <- sapply(campeones[, c(1)], as.numeric)

label_campeones <- campeones
number_of_bar <- nrow(label_campeones)

angle <- 90 - 360 * (label_campeones$id-0.5) /number_of_bar    
label_campeones$hjust <- ifelse( angle < -90, 1, 0)
label_campeones$angle <- ifelse(angle < -90, angle+180, angle)

base_campeones <- campeones %>% 
  group_by(nationality.x) %>% 
  summarise(start=min(id), end=max(id)) %>% 
  rowwise() %>% 
  mutate(title=mean(c(start, end)))

grid_campeones <- base_campeones
grid_campeones$end <- grid_campeones$end[ c( nrow(grid_campeones), 1:nrow(grid_campeones)-1)] + 1
grid_campeones$start <- grid_campeones$start - 1
grid_campeones <- grid_campeones[-1,]

gg_circ_victorias <- ggplot(campeones, aes(x=as.factor(year), y=total_campeonatos, fill=nationality.x, color = nationality.x)) + geom_bar(aes(x=as.factor(id), y=total_campeonatos, fill=nationality.x), stat="identity", alpha=0.5) +
  
  geom_segment(data=grid_campeones, aes(x = 0, y = 8, xend = 32, yend = 8), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_campeones, aes(x = 0, y = 6, xend = 32, yend = 6), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_campeones, aes(x = 0, y = 4, xend = 32, yend = 4), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_campeones, aes(x = 0, y = 2, xend = 32, yend = 2), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  
  annotate("text", x = rep(max(campeones$id),4), y = c(2, 4, 6, 8), label = c("2", "4", "6", "8") , color="white", size=3 , angle=0, fontface="bold", hjust=1) +
  
   geom_bar(aes(x=as.factor(id), y=total_campeonatos, fill=nationality.x), stat="identity", alpha=0.5) +
  ylim(-10,21) +
  theme_minimal() +
  theme(
    legend.position = "none",
    axis.text = element_blank(),
    axis.title = element_blank(),
    panel.grid = element_blank(),
    plot.margin = unit(rep(-1,4), "cm") ) +
  coord_polar() + 
  geom_text(data=label_campeones, aes(x=id, y=10, label=driverRef, hjust=hjust), color="white", fontface="bold",alpha=0.6, size=3.5, angle= label_campeones$angle, inherit.aes = FALSE ) +

  geom_segment(data=grid_campeones, aes(x = 0.70, y = -1, xend = 2.45, yend = -1), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE )  +
  geom_segment(data=grid_campeones, aes(x = 2.6, y = -1, xend = 3.55, yend = -1), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_campeones, aes(x = 3.65, y = -1, xend = 5.45, yend = -1), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_campeones, aes(x = 5.55, y = -1, xend = 7.35, yend = -1), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_campeones, aes(x = 7.5, y = -1, xend = 10.50, yend = -1), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_campeones, aes(x = 10.7, y = -1, xend = 19.20, yend = -1), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_campeones, aes(x = 19.4, y = -1, xend = 20.3, yend = -1), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_campeones, aes(x = 20.45, y = -1, xend = 23.4, yend = -1), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) + 
  geom_segment(data=grid_campeones, aes(x = 23.65, y = -1, xend = 24.35, yend = -1), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) + 
  geom_segment(data=grid_campeones, aes(x = 24.60, y = -1, xend = 27, yend = -1), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_campeones, aes(x = 27.2, y = -1, xend = 29.5, yend = -1), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_campeones, aes(x = 29.7, y = -1, xend = 30.5, yend = -1), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) + 
  geom_segment(data=grid_campeones, aes(x = 30.7, y = -1, xend = 31.5, yend = -1), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_campeones, aes(x = 31.7, y = -1, xend = 32.5, yend = -1), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_campeones, aes(x = 32.7, y = -1, xend = 33.5, yend = -1), colour = "white", alpha=1, size=0.3 , inherit.aes = FALSE ) + theme(legend.text = element_text(colour = "white"),
    legend.title = element_text( colour = "white"), 
     legend.background = element_rect(fill = "gray13", colour = "gray13"),
    legend.key = element_rect(fill = "gray13"),
    legend.position = "right",
    panel.background = element_rect(fill = "gray13" , colour = "gray13"),
    plot.background = element_rect(fill = "gray13" , colour = "gray13"))+labs(colour = "Nacionalidad", fill = "Nacionalidad")
#gg_circ_victorias
#las siguientes lineas de codigo se utilizan para eliminar los bordes blancos
ggsave("./plots/gg_circ_victorias.png", width = 8, height = 5)
```

<center>


![Campeones del mundo](./plots/gg_circ_victorias.png){width=800 height=600}


</center>


## 3.3 Temporada 2021

### Campeón del mundo{.tabset}

#### Verstappen

<center>
![Verstappen - Nuevo Campeón 2021](./imagenes/CAMPEON/campeon_verstappen.jpg){width=640 height=800}
</center>

#### Final de infarto

```{r}

salida <- image_read("./imagenes/CAMPEON/hamilton_adel.jpg") %>% image_scale(., "500")%>%  image_annotate(., "Hamilton se pone por delante", size = 20, gravity = "northwest", color = "cyan", boxcolor = "gray13")
latifi <- image_read("./imagenes/CAMPEON/latifi.jpg") %>% image_scale(., "500")%>%  image_annotate(., "Accidente de Latifi", size = 40, gravity = "northwest", color = "cyan", boxcolor = "gray13")
safety <- image_read("./imagenes/CAMPEON/safety.jpg") %>% image_scale(., "500")%>%  image_annotate(., "Sale el safety car", size = 40, gravity = "northwest", color = "cyan", boxcolor = "gray13")
rueda_a_rueda <- image_read("./imagenes/CAMPEON/ruedaarueda.jpeg") %>% image_scale(., "500")%>%  image_annotate(., "Igualdad máxima", size = 30, gravity = "northwest", color = "cyan", boxcolor = "gray13")
meta <- image_read("./imagenes/CAMPEON/verstappen_gana.jpg") %>% image_scale(., "500")%>%  image_annotate(., "Verstappen se hace con la victoria", size = 20, gravity = "northwest", color = "cyan", boxcolor = "gray13")
trofeo <- image_read("./imagenes/CAMPEON/campeon_trofeo.jpg") %>% image_scale(., "500")%>%  image_annotate(., "Nuevo campeón del mundo", size = 30, gravity = "northwest", color = "cyan", boxcolor = "gray13")
last_race <- c(salida, latifi, safety, rueda_a_rueda, meta, trofeo)

image_animate(image_scale(last_race), fps = 0.5)
```

#### Verstappen vs Hamilton

```{r}
vers_vs_ham <- data.frame(
  "nombre" = c("Max Verstappen", "Lewis Hamilton"),
  "piloto" = c("./imagenes/pilotos/verstappen.jpg","./imagenes/pilotos/hamilton.png"),
  "pais" = c("./imagenes/paises/holanda.png", "./imagenes/paises/uk.png"),
  "escuderia" = c("./imagenes/escuderias/redbull.png", "./imagenes/escuderias/mercedes.png"),
  "puntos" = c(395.5, 387.5),
  "victorias" = c(10, 8),
  "podiums" = c(18, 17),
  "poles" = c(10, 5),
  "vueltas_rapidas" = c(6, 6))
  

 
tabla_ver_ham <- vers_vs_ham %>% gt() %>% 
  text_transform( locations = cells_body(columns = c(piloto)), fn = function(x) {gt::local_image(x, height = 80)}) %>% 
  text_transform( locations = cells_body(columns = c(pais)), fn = function(x) {gt::local_image(x, height = 60)}) %>%  
  text_transform( locations = cells_body(columns = c(escuderia)), fn = function(x) {gt::local_image(x, height = 80)}) %>% 
  tab_header(title = md("**Verstappen vs Hamilton**"), subtitle = md("2021")) %>%   cols_label(
    nombre = html(""),
    piloto = html(""),
    pais = html(""),
    escuderia = html("Escudería"),
    puntos = html("Puntos"),
    victorias = html("Victorias"),
    podiums = html("Podiums"),
    poles = html("Poles"),
    vueltas_rapidas = html("Vueltas rápidas")) %>%  
  tab_options(table.background.color = "gray13",   table.font.color.light = "cyan") %>% 
  cols_align(align = "center",
  columns = everything())


tabla_ver_ham
```


### Parrilla de pilotos 

```{r}

pilotos_2021 <- full_join(carreras, resultados, c("raceId" = "raceId") ) %>% 
  full_join(.,pilotos, c("driverId"="driverId")) %>% 
  filter(year=="2021") %>% 
  select(forename,surname, year) %>% distinct(forename,surname) %>% na.omit() %>% arrange(surname) %>% str_replace_all(., "Ã©", "é") %>% str_replace_all(., "Ã¤", "ä") %>% str_replace_all(., "Ã¶", "ö")

nombres_normales <- c("Alonso", "Bottas", "Gasly", "Giovinazzi", "Hamilton", "Latifi", "Leclerc", "Mazepin", "Norris", "Ocon", "Pérez", "Räikkönen", "Ricciardo", "Russell", "Sainz", "Schumacher", "Stroll", "Tsunoda", "Verstappen", "Vettel")

pilotos_2021 <-  full_join(carreras, resultados, c("raceId" = "raceId") ) %>% 
  full_join(.,pilotos, c("driverId"="driverId")) %>% 
  filter(year=="2021") %>% 
  select(forename,surname, year) %>% distinct(forename,surname) %>% na.omit() %>% arrange(surname) %>% add_column(nombres_normales)  
 

# PÃ©rez
# RÃ¤ikkÃ¶nen
fotos_pil_2021 <- c("./imagenes/pilotos/alonso.png", "./imagenes/pilotos/bottas.png", "./imagenes/pilotos/gasly.png", "./imagenes/pilotos/giovinazzi.jpg", "./imagenes/pilotos/hamilton.png", "./imagenes/pilotos/latifi.png", "./imagenes/pilotos/leclerc.png", "./imagenes/pilotos/mazepin.png", "./imagenes/pilotos/norris.png", "./imagenes/pilotos/ocon.jpg", "./imagenes/pilotos/perez.png", "./imagenes/pilotos/raikkonen.png", "./imagenes/pilotos/ricciardo.png", "./imagenes/pilotos/russell.png", "./imagenes/pilotos/sainz.png", "./imagenes/pilotos/mick.png", "./imagenes/pilotos/stroll.png", "./imagenes/pilotos/tsunoda.png", "./imagenes/pilotos/verstappen.jpg", "./imagenes/pilotos/vettel.png")

fotos_pais_2021 <- c("./imagenes/paises/espanya.png", "./imagenes/paises/finlandia.png", "./imagenes/paises/francia.png", "./imagenes/paises/italia.png","./imagenes/paises/uk.png", "./imagenes/paises/canada.png","./imagenes/paises/monaco.png", "./imagenes/paises/rusia.png","./imagenes/paises/uk.png", "./imagenes/paises/francia.png","./imagenes/paises/mexico.png", "./imagenes/paises/finlandia.png","./imagenes/paises/australia.png", "./imagenes/paises/uk.png","./imagenes/paises/espanya.png", "./imagenes/paises/alemania.png","./imagenes/paises/canada.png", "./imagenes/paises/japon.png","./imagenes/paises/holanda.png", "./imagenes/paises/alemania.png")

fotos_esc_2021 <- c("./imagenes/escuderias/alpine.png","./imagenes/escuderias/mercedes.png","./imagenes/escuderias/alphatauri.png", "./imagenes/escuderias/alfaromeo.jpg","./imagenes/escuderias/mercedes.png", "./imagenes/escuderias/williams.png","./imagenes/escuderias/ferrari.png", "./imagenes/escuderias/haas.png","./imagenes/escuderias/mclaren.png", "./imagenes/escuderias/alpine.png","./imagenes/escuderias/redbull.png", "./imagenes/escuderias/alfaromeo.jpg","./imagenes/escuderias/mclaren.png", "./imagenes/escuderias/williams.png","./imagenes/escuderias/ferrari.png", "./imagenes/escuderias/haas.png","./imagenes/escuderias/aston.png", "./imagenes/escuderias/alphatauri.png","./imagenes/escuderias/redbull.png", "./imagenes/escuderias/aston.png")

pilotos_2021 <- pilotos_2021 %>%
  add_column(fotos_pil_2021, fotos_pais_2021, fotos_esc_2021) %>% select(nombres_normales, fotos_pil_2021, fotos_pais_2021, fotos_esc_2021)


mundial_2021 <- pilotos_2021 %>% gt() %>% text_transform( locations = cells_body(columns = c(fotos_pil_2021)), fn = function(x) {gt::local_image(x, height = 100)}) %>% text_transform( locations = cells_body(columns = c(fotos_pais_2021)), fn = function(x) {gt::local_image(x, height = 50)}) %>% text_transform( locations = cells_body(columns = c(fotos_esc_2021)), fn = function(x) {gt::local_image(x, height = 70)}) %>% tab_header(title = md("**Pilotos 2021**"), subtitle = md("Parrilla")) %>%   cols_label(
    nombres_normales = html(""),
    fotos_pil_2021 = html(""),
    fotos_pais_2021 = html(""),
    fotos_esc_2021 = html("")) %>%  
  tab_options(table.background.color = "gray13",   table.font.color.light = "cyan") %>% 
  cols_align(align = "center",
  columns = everything())


#mundial_2021
```


### Templos

```{r, eval = TRUE, echo = TRUE}

globo_circ <-create_globe() %>% globe_pov(45.61560, 9.281110) %>% globe_bars(coords(lat, lng, label  = variables, color = round2), data = carreras_21_v2)  %>% scale_bars_color()
#globo_circ
```



## 3.4 El nano

### Alonso vs compañeros de equipo

```{r, eval = TRUE, echo = TRUE}
gc() #instruccion para que cargue el grafico, al ser tan complejo da error de no sé qué pero con esto funciona
ggalo_vs_all <- ggplot(data = ALO_VS_ALL, aes(round, puntos_acumulados, color = driverRef)) +
  geom_line() +
  geom_point() + 
  labs(title = "Alonso contra el mundo",
       subtitle = "Alonso VS cada compañero de equipo",
       y = "Puntos", x = "") + theme(axis.line = element_line(colour = "gray30",
    linetype = "solid"), axis.ticks = element_line(colour = "gray30"),
    panel.grid.major = element_line(colour = "gray20"),
    panel.grid.minor = element_line(linetype = "blank"),
    axis.title = element_text(size = 15,
        colour = "white"), axis.text = element_text(colour = "white"),
    plot.title = element_text(size = 20,
        colour = "white", hjust = 0.5), legend.text = element_text(colour = "white"),
    legend.title = element_text(colour = "white"),
    panel.background = element_rect(fill = "gray13",
        colour = "gray13", linetype = "solid"),
    plot.background = element_rect(fill = "gray13",
        colour = "gray13", linetype = "solid"),
    legend.key = element_rect(fill = "gray13",
        colour = "gray13"), legend.background = element_rect(fill = "gray13",
        colour = "gray13")) +labs(x = "Nº carrera", colour = "Piloto") + theme(plot.subtitle = element_text(size = 10,
    hjust = 0.5)) + facet_wrap( ~ year) + transition_reveal(round)

#ggalo_vs_all

```

### Alonso vs Hamilton
```{r, eval=TRUE, echo=TRUE}

#se necesita tener cargado "n_carreras_nom", "victorias_con_nombre"
#1234

alo_vs_ham_tabla <- alo_vs_ham %>% gt() %>% text_transform( locations = cells_body(columns = c(fotos_esp_ing)), fn = function(x) {gt::local_image(x, height = 50)}) %>% text_transform( locations = cells_body(columns = c(fotos_ALO_vs_HAM)), fn = function(x) {gt::local_image(x, height = 100)}) %>% tab_header(title = md("**Alonso vs Hamilton**"), subtitle = md("Comparación")) %>%   cols_label(
    driverRef = html(""),
    numero_carreras = html("Nº carreras"),
    n_victorias = html("Nº victorias"),
    fotos_esp_ing = html("País"),
    fotos_ALO_vs_HAM = html("")) %>%  
  tab_options(table.background.color = "gray13",   table.font.color.light = "cyan") %>% 
  cols_align(align = "center",
  columns = everything())

alo_vs_ham_tabla
```





# 4. Trabajos en los que te has basado
Con los datos que hemos encontrado, existen una serie de códigos que ya trabajan con estos datos, especialmente [este](https://www.kaggle.com/ekrembayar/formula-1-70th-anniversary), a partir del cual, sobre todo, nos hemos fijado en la estética de los gráficos y las imagenes con las que los acompañaba. También hemos obtenido ideas para trabajar los datos, pero en menor medida que el apartado visual, ya que el trabajo citado se centra unicamente en la temporada actual, mientras que **Alt+ Formula 1** combina tanto la actualidad como la historia en general de la F1.


# 5. Bibliografía

La gran parte del trabajo se ha centrado en los datos obtenidos a partir de Kaggle, por lo que no son muchas las páginas auxiliares que hemos utilizado.  
Para la obtención de las audiencias se han utilizado tanto [esta](https://www.thebestf1.es/analisis-asi-han-evolucionado-las-audiencias-en-espana-desde-la-2-a-movistar-f1/) página web , como [este](https://twitter.com/MedF1osTV/status/1376462834842923008) post de twitter. 

